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.

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.

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.

Reliability Analysis of Corroding Pipelines Considering the Growth and Generation of Corrosion Defects

2014 ◽  
Author(s):  
H. Qin ◽  
W. Zhou
Keyword(s):  
Poisson Process ◽  
Reliability Analysis ◽  
Measurement Errors ◽  
Gamma Process ◽  
Homogeneous Poisson Process ◽  
Monte Carlo Simulation Technique ◽  
Natural Gas Pipeline ◽  
Corrosion Defects ◽  
Inspection Data ◽  
Non Homogeneous Poisson Process

This paper presents a methodology to evaluate the reliability of corroding pipelines by simultaneously considering the growth and generation of corrosion defects. The non-homogeneous Poisson process is employed to model the generation of corrosion defects, whereas the non-homogeneous gamma process is used to characterize the growth of corrosion defects once generated. The parameters included in the non-homogeneous Poisson process and non-homogeneous gamma process are evaluated from the inline inspection data using a hierarchical Bayesian model. The measurement errors associated with the inline inspection tools are taken into account in the Bayesian updating. The time-dependent failure probability of the corroding pipeline is evaluated using the Monte Carlo simulation technique. The methodology is illustrated using a natural gas pipeline that has been subjected to multiple inline inspections over a period of time. The results illustrate the necessity to incorporate the generation of new corrosion defects in the reliability analysis of corroding pipelines.

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.

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.

History and Development of U.S. Procedures for Falling Weight Deflectometer Calibration

2005 ◽  
Vol 1905 (1) ◽  
pp. 66-72 ◽  
Author(s):  
Lynne H. Irwin ◽  
Cheryl A. Richter
Keyword(s):  
Measurement Errors ◽  
Acceptance Testing ◽  
Bias Error ◽  
Systematic Bias ◽  
Falling Weight Deflectometer ◽  
Random Measurement Error ◽  
Random Measurement ◽  
Calibration Protocol ◽  
Falling Weight Deflectometers ◽  
Falling Weight

In 1988 the Strategic Highway Research Program purchased four falling weight deflectometers (FWDs). During the acceptance testing it became evident that an improved procedure for calibration was needed to determine whether the specifications for the precision and the accuracy of the sensors were achieved. The authors were responsible for developing the procedure. This paper reports on the steps taken during the development of the calibration protocol. The reasons underlying the equipment and the procedures chosen are discussed. The sources of error in FWD measurements are identified, and ways that have been used to reduce those errors are reported. The goal was to reduce the systematic (bias) error to less than 0.3% through calibration. This level of error ensures that the random measurement error is larger than the systematic error for all pavement deflections less than 600 μm [24 mils (1 mil = 0.001 in.)]. Experience has shown that most highway pavements deflect less than 600 μm. The effects of FWD measurement errors on backcalculated pavement moduli are briefly reviewed. Verification of the protocol by several means showed that the calibration goal was achieved. Subsequent experience with the calibration protocol has shown that it has been effective and that it ensures high quality in the FWD data.

Analysis of the random measurement error of areal 3D coordinate measurements exclusively based on measurement repetitions

2021 ◽  
Vol 88 (2) ◽  
pp. 71-77
Author(s):  
Andreas Michael Müller ◽  
Tino Hausotte
Keyword(s):  
Measurement Error ◽  
Measurement Uncertainty ◽  
Measurement System ◽  
Measurement Errors ◽  
Random Measurement Error ◽  
Additional Information ◽  
Random Measurement ◽  
Random Components ◽  
Measurement Object ◽  
X Ray Computed

Abstract The measurement uncertainty characteristics of a measurement system are an important parameter when evaluating the suitability of a certain measurement system for a specific measurement task. The measurement uncertainty can be calculated from observed measurement errors, which consist of both systematic and random components. While the unfavourable influence of systematic components can be compensated by calibration, random components are inherently not correctable. There are various measurement principles which are affected by different measurement error characteristics depending on specific properties of the measurement task, e. g. the optical surface properties of the measurement object when using fringe projection or the material properties when using industrial X-ray computed tomography. Thus, it can be helpful in certain scenarios if the spatial distribution of the acquisition quality as well as uncertainty characteristics on the captured surface of a certain measurement task can be found out. This article demonstrates a methodology to determine the random measurement error solely from a series of measurement repetitions without the need of additional information, e. g. a reference measurement or the nominal geometry of the examined part.

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.

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.

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