Quantification of Measurement Errors in the Lengths of Metal-Loss Corrosion Defects Reported by Inline Inspection Tools

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
T. Siraj ◽  
W. Zhou
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Logistic Model ◽  
Oil And Gas ◽  
Metal Loss ◽  
Type I ◽  
Large Set ◽  
Type Ii ◽  
Oil And Gas Pipelines ◽  
Corrosion Defects

Abstract This paper proposes a framework to quantify the measurement error associated with lengths of corrosion defects on oil and gas pipelines reported by inline inspection (ILI) tools based on a relatively large set of ILI-reported and field-measured defect data collected from different in-service pipelines in Canada. A log-logistic model is proposed to quantify the likelihood of a given ILI-reported defect being a type I defect (without clustering error) or a type II defect (with clustering error). The measurement error associated with the ILI-reported length of the defect is quantified as the average of those associated with the types I and II defects, weighted by the corresponding probabilities obtained from the log-logistic model. The implications of the proposed framework for the reliability analysis of corroded pipelines given the ILI information are investigated using a realistic pipeline example.

Time-Dependent Corrosion Growth Modeling Using Multiple ILI Data

2012 ◽  
Author(s):  
S. Zhang ◽  
W. Zhou ◽  
M. Al-Amin ◽  
S. Kariyawasam ◽  
H. Wang
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Oil And Gas ◽  
Time Dependent ◽  
Random Measurement Error ◽  
Random Measurement ◽  
Proposed Model ◽  
Oil And Gas Pipelines ◽  
Corrosion Defects ◽  
Pipeline Corrosion

This paper describes a non-homogeneous 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.

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.

The Effect of Noisy Fair Value Measures on Bank Capital Adequacy Ratios

2013 ◽  
Vol 27 (4) ◽  
pp. 693-710 ◽  
Author(s):  
Adrian Valencia ◽  
Thomas J. Smith ◽  
James Ang
Keyword(s):  
Measurement Error ◽  
Deposit Insurance ◽  
Type I Error ◽  
Fair Value ◽  
Capital Adequacy ◽  
Type I ◽  
Type Ii ◽  
Bank Capital ◽  
Recent Financial Crisis ◽  
Type Ii Errors

SYNOPSIS Fair value accounting has been a hotly debated topic during the recent financial crisis. Supporters argue that fair values are more relevant to investors, while detractors point to the measurement error in the estimation of the reported fair values to attack its reliability. This study examines how noise in reported fair values impacts bank capital adequacy ratios. If measurement error causes reported capital levels to deviate from fundamental levels, then regulators could misidentify a financially healthy bank as troubled (type I error) or a financially troubled bank as safe (type II error), leading to suboptimal resource allocations for banks, regulators, and investors. We use a Monte Carlo simulation to generate our data, and find that while noise leads to both type I and type II errors around key Federal Deposit Insurance Corporation (FDIC) capital adequacy benchmarks, the type I error dominates. Specifically, noise is associated with 2.58 (2.60) [1.092], 5.67 (6.44) [1.94], and 10.60 (26.83) [3.423] times more type I errors than type II errors around the Tier 1 (Total) [Leverage] well-capitalized, adequately capitalized, and significantly undercapitalized benchmarks, respectively. Economically, our results suggest that noise can lead to inefficient allocation of resources on the part of regulators (increased monitoring costs) and banks (increased compliance costs). JEL Classifications: D52; M41; C15; G21.

Fitness for Service Assessment of Cross Country Oil Pipelines Based on API 579 (Application of API 579 on ASME B 31.4)

2019 ◽  
Author(s):  
Pradeep Purnana ◽  
Shiyas Ibrahim
Keyword(s):  
Oil And Gas ◽  
Internal Surface ◽  
Life Assessment ◽  
Metal Loss ◽  
Complex Method ◽  
General Corrosion ◽  
Loss Assessment ◽  
Oil Pipelines ◽  
Corrosion Defects ◽  
Cross Country

Pipelines are one of the safest forms of transportation for oil and gas. However, Pipelines may experience defects, such as corrosion, cracks during service period. Therefore, evaluation of these defects is very important in terms of assessment and for continued safe operation. Corrosion defects at the external surface of pipelines are often the result of fabrication faults, coating or cathodic protection issues, residual stress, cyclic loading, temperature or local environment (soil chemistry). In general, corrosion may occur in most pipes due to coating failure, and a pipe without any protective coating will experience external corrosion after some years. However, corrosion can occur on the internal surface of the pipeline due to contaminants in the products such as small sand particles. At present, there are different assessment methods for different types of defects in pipelines. The most popular codes for defect assessment in oil and gas pipelines are RSTRENG, Modified B31G, BS 7910 and API 579. Besides these codes and methods, there are numerical programs, such as CorLAS, which have been used successfully for assessing crack flaws in Pipelines. RSTRENG and B 31G methods are very simple when compared with API 579. API 579 is very complex method of assessing defects but very useful for remaining life assessment of Pipelines. In this paper corrosion defects like general metal loss, localized metal loss, pitting corrosion, other defects like dents, gouges, cracks, their remediation methods assessed based on API 579 method and our experience in Oil Pipelines. Since API 579 doesn’t cover cross country pipelines explicitly, we have made a research applying API 579 to ASME B31.4. Even though, we have done research on all types of defects (Level 1 and Level 2 assessment), in this paper we have covered only General metal loss assessment.

scholarly journals A Real-Time, Non-Contact Method for In-Line Inspection of Oil and Gas Pipelines Using Optical Sensor Array

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3615 ◽  
Author(s):  
Santhakumar Sampath ◽  
Bishakh Bhattacharya ◽  
Pouria Aryan ◽  
Hoon Sohn
Keyword(s):  
Real Time ◽  
Optical Sensor ◽  
Sensor Array ◽  
Oil And Gas ◽  
Maintenance Cost ◽  
Physical Parameters ◽  
Gas Pipelines ◽  
Inner Surface ◽  
Oil And Gas Pipelines ◽  
Corrosion Defects

Corrosion is considered as one of the most predominant causes of pipeline failures in the oil and gas industry and normally cannot be easily detected at the inner surface of pipelines without service disruption. The real-time inspection of oil and gas pipelines is extremely vital to mitigate accidents and maintenance cost as well as to improve the oil and gas transport efficiency. In this paper, a new, non-contact optical sensor array method for real-time inspection and non-destructive evaluation (NDE) of pipelines is presented. The proposed optical method consists of light emitting diodes (LEDs) and light dependent resistors (LDRs) to send light and receive reflected light from the inner surface of pipelines. The uniqueness of the proposed method lies in its accurate detection as well as its localization of corrosion defects, based on the utilization of optical sensor array in the pipeline, and also the flexibility with which this system can be adopted for pipelines with different services, sizes, and materials, as well as the method’s economic viability. Experimental studies are conducted considering corrosion defects with different features and dimensions to confirm the robustness and accuracy of the method. The obtained data are processed with discrete wavelet transform (DWT) for noise cancelation and feature extraction. The estimated sizes of the corrosion defects for different physical parameters, such as inspection speed and lift-off distance, are investigated and, finally, some preliminary tests are conducted based on the implementation of the proposed method on an in-line developed smart pipeline inspection gauge (PIG) for in-line inspection (ILI) application, with resulting success.

Bayesian Model for Calibration of ILI Tools

2012 ◽  
Author(s):  
M. Al-Amin ◽  
W. Zhou ◽  
S. Zhang ◽  
S. Kariyawasam ◽  
H. Wang
Keyword(s):  
Measurement Error ◽  
Linear Function ◽  
Bayesian Model ◽  
Measurement Data ◽  
Metal Loss ◽  
Bayesian Methodology ◽  
Field Measuring ◽  
Corrosion Defects ◽  
Measuring Tool ◽  
Actual Depth

The Bayesian methodology is employed to calibrate the accuracy of high-resolution ILI tools for sizing metal-loss corrosion defects on pipelines by comparing the field-measured depths and ILI-reported depths for a set of static defects, i.e. defects that are recoated and ceased growing. The measurement error associated with the field-measuring tool is found to be negligibly small; therefore, the field-measured depth is assumed to equal the actual depth of the defect. The depth of a corrosion defect reported by an ILI tool is assumed to be a linear function of the corresponding field-measured depth subjected to a random scattering error. The probabilistic characteristics of the intercept and slope in the linear function, i.e. the constant and non-constant biases of the measurement error, as well as the standard deviation of the random scattering error are then quantified using the Bayesian methodology. The proposed methodology is able to calibrate the accuracies of multiple ILI tools simultaneously and quantify the potential correlations between the accuracies of different ILI tools. The methodology is illustrated using real ILI and field measurement data obtained on two pipelines currently in service.

A Study on the Safety Factor for Corrosion Assessment of Oil and Gas Pipeline through In-Line Inspection

2019 ◽  
Vol 795 ◽  
pp. 233-238 ◽  
Author(s):  
Jian Chen ◽  
Ming Fei Li ◽  
Ju Hong Wang ◽  
Xue Li Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Safety Factor ◽  
Oil And Gas ◽  
Common Type ◽  
Safety Factors ◽  
Integrity Assessment ◽  
Oil And Gas Pipelines ◽  
Corrosion Defects ◽  
Corrosion Assessment ◽  
Selection Of

Corrosion has been the most common type of defects on oil and gas pipelines. For in-service pipelines the corrosion defects are detected through the in-line inspection and evaluated by integrity assessment methods such as ASME B31G. However, the safety factors of these methods have not been carefully studied. In this paper, the Monte Carlo simulation is carried out to estimate the reliability of the corrosion defects of critical sizes with different safety factors. The results show that the reliabilities of the critical defects increase with the increase of the safety factor, but decrease with the increase of the defect length even under the same safety factor. The commonly used safety factor 1.39 can ensure the target reliability is met in the specified case in this paper. But for high consequence cases the selection of safety factor needs further research.

Smart Pig Defect Tolerances: Quantifying the Benefits of Standard and High Resolution Pigs

2004 ◽  
Author(s):  
Stephen Westwood ◽  
Phil Hopkins
Keyword(s):  
High Resolution ◽  
Signal Analysis ◽  
Oil And Gas ◽  
Management Plan ◽  
Major Effect ◽  
Metal Loss ◽  
Confidence Levels ◽  
Failure Assessment ◽  
Oil And Gas Pipelines ◽  
Integrity Management

Smart pigs are used as part of an integrity management plan for oil and gas pipelines to detect metal loss defects. The pigs do not measure the defects: they collect signals from on board equipment and these signals are later analysed. Signal analysis is complex; consequently, defect sizing tolerances and confidence levels can be difficult to determine and apply in practice. They have a major effect when assessing the significance of the defect, and when calculating corrosion growth rates from the results of multiple inspections over time. This paper describes how defect sizing tolerances and confidence levels are obtained by pigging companies, and compares standard and high resolution pigs. Probability theory is used by the authors to estimate the likelihood that a defect is smaller or deeper than the reported (by the pig) value for both standard and high resolution tools. The paper also shows how these tolerances can be included in defect failure assessment and the results of multiple pig runs.

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