Application of MFL on Girth-Weld Defect Detection of Oil and Gas Pipelines

Dai, Lei; Feng, Qingsong; Sutherland, Jeff; Wang, T.; Sha, Shengyi; Wang, F. X.; Wang, D. P.

doi:10.1061/(asce)ps.1949-1204.0000497

Cited by 17 publications

(8 citation statements)

References 8 publications

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“…Hubert Lindner led an ILI project targeting high-pressure, multi-diameter, and heavy-wall subsea Taking China as a focal point, CNOOC has demonstrated a long-standing commitment to MFL equipment innovation, with a growing emphasis on indigenous development. Over the years, they have achieved notable advancements in technology, enhancing their competitive edge in the global arena [63][64][65]. The collaborative effort between China Aerospace Science and Industry Corporation (CASIC), based in Beijing, China, and CNOOC, also headquartered in Beijing, China, has resulted in a self-developed MFL detector that is poised to dramatically reduce the cost of submarine pipeline inspection by 30-50%.…”

Section: Magnetic Flux Leakage (Mfl)mentioning

confidence: 99%

In-Line Inspection (ILI) Techniques for Subsea Pipelines: State-of-the-Art

Zhu,

Chen,

Lin

et al. 2024

JMSE

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Offshore oil and gas resources play a crucial role in supplementing the energy needs of human society. The crisscrossing subsea pipeline network, which serves as vital infrastructure for the storage and transportation of offshore oil and gas, requires regular inspection and maintenance to ensure safe operation and prevent ecological pollution. In-line inspection (ILI) techniques have been widely used in the detection and inspection of potential hazards within the pipeline network. This paper offers an overview of ILI techniques used in subsea pipelines, examining their advantages, limitations, applicable scenarios, and performance. It aims to provide valuable insights for the selection of ILI technologies in engineering and may be beneficial for those involved in pipeline integrity management and planning.

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Section: Magnetic Flux Leakage (Mfl)mentioning

confidence: 99%

In-Line Inspection (ILI) Techniques for Subsea Pipelines: State-of-the-Art

Zhu,

Chen,

Lin

et al. 2024

JMSE

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“…The cracking of circumferential welds in oil and gas pipelines is characterized by easy propagation and a high risk of explosion after initiation [7][8][9][10] . Once a circumferential weld failure occurs, it poses a severe threat to individuals and the environment along the pipeline route, leading to substantial economic losses [11][12][13][14][15] .…”

Section: Introductionmentioning

confidence: 99%

Mechanical structure design and simulation of multi-physical field integrated pipeline internal inspection detector

Huang,

Chen,

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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“…In 1947, Hastings designed and constructed the first functional MFL detection device. In 1965, the Tuboscope company pioneered the world's first internal pipelines MFL detector, which demonstrated the capability to accurately detect defects and facilitate qualitative defect analysis (Dai et al 2020). MFL testing technology technology relies on the high permeability properties of ferromagnetic pipeliness, whose walls can be magnetized to saturation or near-saturation by a uniform magnetic field generated using a permanent magnet or coil.…”

Section: Introductionmentioning

confidence: 99%

Defect detection in pipelines using magnetic flux leakage method combined with Kernel Extreme Learning Machine

Li,

Sun

2024

Preprint

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Magnetic flux leakage (MFL) testing technology is widely employed in non-destructive testing of pipelines, and the analysis of leakage signals plays a crucial role in assessing safety. This paper introduces a novel approach for MFL testing, which combines finite element simulation with artificial neural networks. Firstly, a simulation model is developed to study MFL testing in defective pipelines, with a focus on investigating how magnetization state and defect dimensions impact the leakage signal. Signal features are subsequently defined and extracted through an analysis of the distribution curve of MFL signals. Finally, a Kernel Extreme Learning Machine (KELM) model is constructed, and optimization is carried out on the influential parameters to predict defect dimensions. The results demonstrate that the effect of magnetization intensity on the magnetization state can be categorized into two stages: a nonlinear growth stage and a saturated linear growth stage. Additionally, an increase in defect size has the effect of delaying the magnetization process in pipelines. Notably, changes in defect dimensions have a pronounced impact on the distribution of MFL signals. And KELM prediction model is demonstrated to be capable of accurately and efficiently predicting the depth, length, and area of defects.

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Application of MFL on Girth-Weld Defect Detection of Oil and Gas Pipelines

Cited by 17 publications

References 8 publications

In-Line Inspection (ILI) Techniques for Subsea Pipelines: State-of-the-Art

In-Line Inspection (ILI) Techniques for Subsea Pipelines: State-of-the-Art

Mechanical structure design and simulation of multi-physical field integrated pipeline internal inspection detector

Defect detection in pipelines using magnetic flux leakage method combined with Kernel Extreme Learning Machine

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