Adaptive approach for estimation of pipeline corrosion defects via Bayesian inference

2021 ◽  
Vol 216 ◽  
pp. 107998
Author(s):  
Kyeongsu Kim ◽  
Gunhak Lee ◽  
Keonhee Park ◽  
Seongho Park ◽  
Won Bo Lee
Keyword(s):  
Bayesian Inference ◽  
Adaptive Approach ◽  
Corrosion Defects ◽  
Pipeline Corrosion

Validity of the LAPA Method for Assessment of Defects Reported by In-Line Inspection Tools

2004 ◽  
Author(s):  
Richard Fletcher ◽  
Louis Fenyvesi
Keyword(s):  
Alternative Assessment ◽  
Reliable Method ◽  
Burst Pressure ◽  
Independent Validation ◽  
Defect Profile ◽  
Corrosion Defects ◽  
Pipeline Corrosion

Over recent years, RSTRENG has gained acceptance as a reliable method of assessing the effect of pipeline corrosion defects, while reducing the conservatism inherent in some of the alternative assessment models. The Length Adaptive Pressure Assessment (LAPA) algorithm has been developed to apply an adaptation of the RSTRENG methodology directly to the results of a magnetic in-line inspection. Adoption of the LAPA technique offers substantial accuracy and efficiency benefits over the more conventional processes, but the industry requires confirmation of the method’s validity before it can take advantage of these. The paper will describe the LAPA validation work performed by PII, and independent validation undertaken by TransCanada Pipelines and other pipeline operators. The independent validation took the form of a comparison of LAPA-based burst pressure calculations with RSTRENG calculations based on direct infield measurements of the defect profile. TransCanada also performed a series of burst tests to quantify the accuracy of the LAPA calculations.

Study on the Distribution of Submarine Pipeline Corrosion Defects Based on Internal Inspection Data and Data Mining Method

2020 ◽  
Author(s):  
Guoxi He ◽  
Sijia Chen ◽  
Kexi Liao ◽  
Shuai Zhao
Keyword(s):  
Least Square ◽  
Support Vector ◽  
Submarine Pipeline ◽  
Distribution Rules ◽  
Pipe Segment ◽  
Integrity Management ◽  
Corrosion Defects ◽  
Inspection Data ◽  
Oil Gas ◽  
Pipeline Corrosion

Abstract Submarine pipelines in the sea are applied for oil, gas, water and mixed transportation. Among them, 91% of the pipes contain CO2. Here, based on the existing pipeline internal inspection data of submarine pipeline, the APRIORI algorithm and least-square-support-vector-machine (LSSVM) are applied to analyze the distribution rules and defect characteristics of internal defects along the pipeline. The corrosion defects are divided into 7 types and the pipeline section is divided into 12 intervals. Also, the pipe segment has been defined as J (general pipe), W (weld) and C (close to weld). The contents include the analysis of the characteristics and types of defects, the distribution of defects along the pipe, the severity of the corrosion defects, the size characteristics of defects, and the comparison of the data detected in multiple rounds. The defect depth of four kinds of pipelines is mostly 10%–20% of the wall thickness, hereby the severity of defects is studied via the percentage distribution of corrosion depth. The data of multi-round inspection shows that the corrosions in the mixed pipeline are active and the defects are increasing. The methods and results in this paper can be employed to predict the most likely defect type, mileage location, clock orientation, and shape size of submarine pipeline corrosion. This is helpful for the integrity management of submarine pipelines.

Study on Pulsed Eddy Current Nondestructive Testing Technology for Pipeline Corrosion Defects Based on Finite Element Method

2011 ◽  
Vol 120 ◽  
pp. 36-41 ◽  
Author(s):  
Han Wu Liu ◽  
Shan Ping Zhan ◽  
Yun Hui Du ◽  
Peng Zhang
Keyword(s):  
Magnetic Field ◽  
Finite Element ◽  
Induced Voltage ◽  
Defect Depth ◽  
Element Analysis ◽  
Pulsed Eddy Current ◽  
The Finite Element Analysis ◽  
Corrosion Defects ◽  
Pipeline Corrosion ◽  
Detection Coil

According to the principle and the type of the oil pipeline corrosion, we use the square wave of wide spectrum, strong signal transmission capability and a certain duty ratio as the excitation source of the pulsed eddy current. The finite element analysis software ANSYS is used to establish a three-dimensional finite element model of the pipeline corrosion defects by applying the boundary conditions of square wave excitation to simulate the distributions of current and induced magnetic field in the pipeline under various defect volumes. It can solve the induced voltage variation with time on detection coil, and can accomplish the finite element analysis and the nondestructive testing about the pipeline internal corrosion defects with the insulation layer and the protection layer. The results of the study show: When there is no corrosion defect in the pipeline, the electric current in the pipeline is basically even distribution. The magnetic field is distributed for the symmetrical vortex shape from head to foot, and it has not obviously gather phenomenon. When there are some corrosion defects in the pipeline, the electric current forms partial symmetrical vortex shape in both sides of the corrosion defect, and it is obviously assembled in the defect place. The simulation results of the different size defects show that the maximum magnetic field strength and the maximum current value increase with the defect depth increasing, while the output voltage decreases with the defect depth increasing. By extracting the induced voltage signals on the detection coil in a certain excitation condition, the quantitative detection of the pipeline corrosion defects can be achieved.

Development of a magnetic sensor for detection and sizing of internal pipeline corrosion defects

2009 ◽  
Vol 42 (8) ◽  
pp. 669-677 ◽  
Author(s):  
N.B.S. Gloria ◽  
M.C.L. Areiza ◽  
I.V.J. Miranda ◽  
J.M.A. Rebello
Keyword(s):  
Magnetic Sensor ◽  
Corrosion Defects ◽  
Pipeline Corrosion

Pipeline Corrosion Growth Modeling for In-Line Inspection Data Using a Population-Based Approach

2015 ◽  
Author(s):  
Markus R. Dann ◽  
Marc A. Maes ◽  
Mamdouh M. Salama
Keyword(s):  
Growth Process ◽  
Probabilistic Approach ◽  
Population Based ◽  
Metal Loss ◽  
Small Subset ◽  
Maintenance And Repair ◽  
Small Set ◽  
Corrosion Defects ◽  
Inspection Data ◽  
Pipeline Corrosion

To manage the integrity of corroded pipelines reliable estimates of the current and future corrosion growth process are required. They are often obtained from in-line inspection data by matching defects from two or more inspections and determining corrosion growth rates from the observed growth paths. In practice only a (small) subset of the observed defects are often reliably matched and used in the subsequent corrosion growth analysis. The information from the remaining unmatched defects on the corrosion growth process are typically ignored. Hence, all decisions that depend on the corrosion growth process such as maintenance and repair requirements and re-inspection intervals, are based on the information obtained from the (small) set of matched defects rather than all observed corrosion anomalies. A new probabilistic approach for estimating corrosion growth from in-line inspection data is introduced. It does not depend on defect matching and the associated defect matching uncertainties. The reported defects of an inspection are considered from a population perspective and the corrosion growth is determined from two or more defect populations. The distribution of the reported defect sizes is transformed into the distribution of the actual defect sizes by adjusting it for detectability, false calls, and sizing uncertainties. The obtained distribution is then used to determine the parameters of the assumed gamma-distributed corrosion growth process in order to forecast future metal loss in the pipeline. As defect matching is not required all reported corrosion defects are used in the probabilistic analysis rather than the truncated set of matched defects. A numerical example is provided where two in-line inspections are analyzed.

Time-Dependent Corrosion Growth Modeling Using Multiple In-Line Inspection Data

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Shenwei Zhang ◽  
Wenxing Zhou ◽  
Mohammad Al-Amin ◽  
Shahani Kariyawasam ◽  
Hong Wang
Keyword(s):  
Measurement Errors ◽  
Oil And Gas ◽  
Time Dependent ◽  
Random Measurement Error ◽  
Random Measurement ◽  
Proposed Model ◽  
Oil And Gas Pipelines ◽  
Corrosion Defects ◽  
Inspection Data ◽  
Pipeline Corrosion

This paper describes a nonhomogeneous gamma process-based model to characterize the growth of the depth of corrosion defect on oil and gas pipelines. All the parameters in the growth model are assumed to be uncertain; the probabilistic characteristics of these parameters are evaluated using the hierarchical Bayesian methodology by incorporating the defect information reported by the multiple in-line inspections (ILIs) as well as the prior knowledge about these parameters. The bias and random measurement error associated with the ILI tools as well as the correlation between the measurement errors associated with different ILI tools are taken into account in the analysis. The application of the model is illustrated using an example involving real ILI data on a pipeline that is currently in service. The results suggest that the model in general can predict the growth of corrosion defects reasonably well. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

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