A review on pipeline integrity management utilizing in-line inspection
Mingjiang Xie, Zhigang Tian
Department of Mechanical Engineering, University of Alberta, Edmonton, AB, Canada
Pipelines are widely used in transporting large quantities of oil and gas products over
long distances due to their safety, efficiency and low cost. Integrity is essential for
reliable pipeline operations, for preventing expensive downtime and failures resulting in
leaking or spilling oil or gas content to the environment. Pipeline integrity management is
a program that manages methods, tools and activities for assessing the health conditions
of pipelines and scheduling inspection and maintenance activities to reduce the risks and
costs. A pipeline integrity management program mainly consists of three major steps:
defect detection and identification, defect growth prediction, and risk-based management.
In-line inspections (ILI) are performed periodically using smart pigging tools to detect
pipeline defects such as corrosion and cracks. Significant advances are needed to
accurately evaluate defects based on ILI data, predict defect growth and optimize
integrity activities to prevent pipeline failures, and pipeline integrity management has
drawn extensive and growing research interests. This paper provides a comprehensive
review on pipeline integrity management based on ILI data. Signal processing methods
for defect evaluation for different types of ILI tools are presented. Physics-based models
and data-driven methods for predicting defect growth for pipelines with different
categories of defects are discussed. Models and methods for risk-based integrity
management are also reviewed in this paper. Current research challenges and possible
future research trends in pipeline integrity management are also discussed.
Pipeline integrity management; inline inspection; defect growth; prediction; risk-based
management; corrosion; crack
The corresponding author (email: email@example.com).
Thanks to the advantages of safety, efficiency and low cost, pipelines are widely used in
transporting large quantities of oil and gas products over long distances. Pipelines may
suffer from different types of defects such as corrosion, fatigue cracks, stress corrosion
cracking (SCC), dent, etc. These defects, if not properly managed, may result in pipeline
failures including leak or rupture, which could lead to very expensive downtime and
environment hazards. There are many pipeline incidents every year around the world, and
three of the North America pipelines incidents in 2016 resulted in over 2,000 metric tons
of oil and gas leak and spill. Integrity is the top priority for pipeline operators to ensure
reliable and safe operations of pipelines, to increase productivity, reduce cost, prevent
damage to the environment, support future projects, etc. It is essential to find effective
ways to monitor, evaluate and assure the integrity of the pipeline, and reduce the risk of
leaks and rupture.
For pipelines, we need to ensure safety, security of supply and compliance with relevant
codes and legislation. Procedures and practices are implemented to protect, manage and
maintain the integrity of pipeline systems. Due to the significant severity of pipeline
failures, the core of pipeline integrity management is to keep pipelines in safe operating
conditions. Pipeline integrity tools are developed to improve business performance,
manage risks as well as ensure compliance. Proper pipeline integrity management can
reduce both the probability and consequences of failure and increase the pipeline
companies’ benefits, by properly assessing and managing the defects. Pipeline integrity
program monitors and predicts defects effects and thus adjusts when, where, how, and
what actions need to be taken, such as inspection, maintenance and repair. A good
pipeline integrity program should be able to manage risk successfully, prevent failure
from occurring, control damage effectively, and reduce the overall cost.
A pipeline integrity program generally consists of three major steps:
(1) Defects detection and identification, to obtain defect information through
inspection, monitoring, testing and analysis techniques.
(2) Defect growth prediction, to predict defect growth based on damage prediction
models and the collected data.
(3) Risk-based management, to recommend optimal inspection, maintenance and
repair policies and activities.
Defect information is collected using detection and identification tools. Pipeline
companies can gather defect information through walking along the pipelines by
technical personnel, hydrostatic testing, in-line inspection (ILI), nondestructive
evaluation (NDE), etc. ILI tools are currently the most widely used inspection technology
for detecting and inspecting various types of pipeline defects. In this paper, only ILI tools
will be discussed and other detection techniques will not be covered. Defect growth
prediction is to predict defect growth and when a pipeline failure will occur. There are
different kinds of threats to pipeline integrity: metal loss, cracking, dents, third party
damage, weld, etc. Study on different defect prediction models is the foundation of
effective integrity management. The last step, risk based management, will determine
proper inspection intervals, and maintenance and repair actions. The management models
will also influence the first step and the second step by possibly changing the inspection
actions and defect status. The aim of an integrity program is to achieve accurate defect
prediction and balance the reliability and costs in an effective way.
Some reported studies considered the design stage as a part of pipeline integrity
management process. It is true that pipeline integrity management is a lifecycle approach
which involves the design phase, and better design practices typically lead to better
pipeline integrity assurance. Study on behaviors of different threats in pipelines as well as
inspection and maintenance activities can also give a good feedback to the pipeline
design stage. Palmer and King  and Antaki  provided detailed introductions to the
pipeline design stage. Bai and Bai  introduced life cycle cost modeling for the design
stage of pipeline integrity management. In this paper, though, we will not cover the
pipeline integrity design stage, and will focus on detection, prediction and management
methods and models during the operation stage.
Pipeline integrity management has drawn extensive and growing research interests, and a
large number of studies have been published in conference proceedings and academic
journals on methodologies, models and applications. This paper reviews the research
studies on pipeline integrity management based on ILI data, with an emphasis on models
and methods developed for more effective defect detection, prediction and management.
Some published reviews discussed topics related to subsections of this paper [4–9], with
some of them emphasizing failure mechanisms of one type of defect, while some
focusing more on applications and practices. Compared with the published reviews, this
paper gives more comprehensive and detailed discussions on the methods and models
used in the pipeline integrity management framework, and provides an overview on
strategies for inspecting, predicting and managing all major pipeline threats. Pipeline
integrity management framework and some related case studies were also presented in
[10–14]. Legal issues and demands for pipeline integrity programs were discussed in .
Pipeline integrity management guidelines are developed by American Petroleum Institute
(API) , which conducts studies on petroleum industry and provides standards for oil
and natural gas industry.
The remainder of the paper is organized as follows. Section 2 describes the in-line
inspection (ILI) tools, which are major technologies to detect and identify the defects,
and its performance and applications. Section 3 reviews data-driven and model-based
methods for predicting the growth of different types of defects. Section 4 covers methods
and models for risk-based management. Conclusions and future research trends are
presented in Section 5.
2. ILI inspection tools for defects detection
Due to possible pipeline leakage, environmental damage and high costs of repair and
replacement, accurate pipeline monitoring and inspection becomes essential these days.
Finding and recording data about pipeline integrity is the first step in pipeline integrity
management, and there are a variety of ways to gather information about defects. Varela
et al.  briefly summarized major methodologies, which are not limited to ILI tools,
that are utilized for monitoring and inspecting external corrosion of pipelines and
discussed the pros and cons of major inspection tools. For external corrosion as well as
other types of threats, there are various inspection techniques to record data on the
defects. Pipeline inspection techniques include potential survey techniques, in-line
inspection (ILI) tools, hydrostatic tests, tools for inspecting non-piggable pipelines like
pipeline crawlers, etc. These pipeline inspection techniques were briefly introduced in
[18,19]. ILI tools will be focused on in this paper.
A high-tech smart pigging device is utilized for in-line inspections, which is inserted in
the pipeline and typically pushed through the pipe by the fluid flow from one compressor
station to another. Such a smart electronic device is known as a smart pig in pipeline
industry. This sophisticated electronic device is essentially a robotic computer that
gathers all specific information related to the health condition of the pipeline. The ILI
tools can classify the types of defect and their attributes including orientation of defects,
size (length, width, depth) and specific location (Internal/External) of the defects .
In-line inspection tools can also evaluate pipeline integrity in geohazard areas by
mapping techniques . How to get high-quality reports from ILI data was introduced
Depending on the types of flaws they can detect, ILI tools can be classified as metal loss
tools, crack tools, geometry detection tools, etc. Metal loss defects reported from an ILI
inspection can be categorized into two main types: pressure based and depth based
defects . With depth based defects such as pitting, a pipeline is typically considered
failed when the defect depth reaches 80% of the pipe wall thickness in industry, if there
are no other specific rules such as NG18, even though sometimes the pipeline doesn’t
show any failure behavior. For pressure based defects such as corrosion defects, failure is
determined by the failure pressure, the model uncertainty and the safety factor .
After gathering relevant data through ILI tools, data processing needs to be performed to
minimize data errors and extract useful information. There are a variety of signal
processing techniques and algorithms for different types of ILI tools. In the following
subsections, signal processing technologies and models will also be reviewed for
different ILI tools.
2.1 In-line inspection technologies
A variety of ILI technologies are widely used in the pipeline field, such as Magnetic flux
leakages (MFL), Ultrasonic (UT) tools, Electromagnetic acoustic transducers (EMAT),
Eddy currents testing (ET), etc. Cartz  presented a review of sensor technologies, and
Varela et al.  discussed ILI technologies that can detect external defects. In the
following subsections, main ILI technologies will be reviewed and compared.
2.1.1 Magnetic flux leakages (MFL)
The most widely used tools for in-line inspection of pipeline are MFL tools. This
technology can detect different types of defects, such as missing material and mechanical
damage, and it is particularly widely used for metal loss inspection in a pipeline integrity
management program. MFL inspection tools detect pipeline defects by sensing a local
change in a saturating magnetic field, which is generated by huge magnets. The center of
the MFL tools is the magnetizer. Gloria et al.  presented the development of the
magnetic sensor. Ireland and Torres  provided a finite element modeling of a
circumferential magnetizer under both moving tool and static conditions. The results
showed that the magnetic field profile is very complicated and researchers need to pay
more attention to studying it in order to further develop MFL tools. Various levels of
sensitivity can be chosen based on the testing needs, such as low resolution (standard),
high resolution and extra high resolution . The higher the resolution of the MFL tools,
the higher the detection capability, which also leads to smaller sensor spacing and higher
confidence level of accuracy. But in industry, some companies used standard tools a lot
because they believe they are sufficient, faster and cheaper. Kopp and Willems 
presented a dipole model study of sizing capabilities of MFL tools. As the resolution
getting higher, the number of sensors in the system gets bigger. Although it is the most
common test and it can meet different testing needs, it may cause the permanent
magnetization of pipe after being used and the restriction of the product flow.
Modern, high-resolution MFL inspection tools have the ability to provide very detailed
signals. However, most of the MFL data can be easily influenced by various noise
sources. To address this problem, many researchers proposed MFL sizing models and
analyzed MFL sizing performance. Yeung et al.  discussed a technique to improve
MFL ILI sizing performance and gave two case studies. Sometimes the sizing
performance is more related to the shape of the defects and some sizing algorithms may
give bigger sizing error due to the differences of the geometries. To address this problem,
Miller and Clouston  proposed an MFL sizing model utilizing high-resolution NDE
data to give better performance. Signal processing for MFL data is a key element in MFL
inspection technique. The primary methods for MFL are wavelet transform, fast Fourier
transform (FFT), Wigner distribution, etc. Mao et al.  gave a brief introduction to
MFL signal processing, and they proposed to improve the defect recognition ability
though integrating neural network, data fusion and expert system techniques. Saha et al.
 used wavelet transform to pre-process the raw radial MFL data. Kathirmani et al. 
proposed a three-stage algorithm for the compression of MFL signals, that is practically
feasible and fast. Mean Absolute Deviation, Principal Component Analysis (PCA) and
Discrete Wavelet Transform (DWT) were utilized in stage I, II, III respectively. Adaptive
algorithms were reported for the processing of MFL signals. Joshi et al.  and Afzal
and Udpa  utilized adaptive wavelets to obtain and process MFL technique signals. Ji
et al.  employed a fuzzy threshold filter algorithm with adaptive wavelets to process
MFL data, and the errors of MFL signals were reduced compared with traditional wavelet
transform. Carvalho et al.  utilized artificial neural networks (ANNs) to classify MFL
signals into signals with defects and signals without defects, and classified the defect
signals into external corrosion (EC), internal corrosion (IC), and lack of penetration (LP)
with high reliability. Chen et al.  presented a empirical mode decomposition (EMD)
based method for signal processing of MFL data. Mukherjee et al.  proposed a new
algorithm of adaptive channel equalization for MFL signal to modify sensor
imperfections, which could recover the signal successfully and minimize noises
2.1.2 Ultrasonic (UT) tools
Currently, ultrasonic is the most reliable in-line inspection technology compared with the
other technologies. Ultrasonic inspection generates ultrasonic pulses of high frequency
and short wavelength to detect defects or measure pipeline wall thickness. In general,
ultrasonic tools give better results and defect accuracy than MFL. The types of flaws UT
can detect include internal/external metal loss, cracking, wall thickness variations, etc.
They are widely used for detecting stress corrosion cracking and many forms of corrosion.
The corrosion penetration depth measurement detection capabilities of UT tools is around
±0.3 to ±0.6 mm , and for longitudinal and circumferential resolution, it is around 3
mm and 8 mm, respectively. The confidence level is at around 95%, which is more
reliable than MFL . Lei et al.  introduced the ultrasonic in-line inspection pig,
which was used for corroded pipelines, and provided the introduction to design stage of
the data acquisition system (DAS) as well as the off-line signal processing method. A
latest generation of ultrasonic ILI tools were presented in , which had high inspection
velocities and high resolution, and, as a result, production loss could be avoided and
confidence level of inspection results could be improved. In addition, the reported tool
generation could deal with higher temperature, higher pressure and bigger speed and wall
thickness ranges. UT tools need to be further developed to give more integrity benefits. A
brief overview and a case study on ultrasonic phased-array (USCD) technology for the
Centennial pipeline stress corrosion cracking were given in . A multilayer
data-driven monitoring framework based on signal processing and machine learning
techniques was introduced by Ying et al. . Bo et al.  gave an introduction to an
ultrasonic ILI system for pipelines.
Compared with other tools, UT gives reliable defect depth sizing and good repeatability,
and it can deal with very small pipeline wall thickness. Compared with MFL tools, UT
tools are also sensitive to a larger variety of features. However, UT tools have the
drawback that they require liquid coupling between the pipeline and the transducer (pig),
which as a result affects its applications in gas pipelines.
The ultrasonic signals collected by UT tools in pipelines are typically noisy. Effective
de-noising techniques are needed to get accurate information regarding defects. The main
signal processing methods used for UT signals are wavelet transform [44,45], artificial
neural networks (ANNs) [46,47], fast Fourier transform (FFT), etc. Song and Que 
developed a new technique based on wavelet transform for processing heavy noised
ultrasonic signals for the purpose of band-pass filter to get better de-noising results.
Martinez et al.  employed several digital processing techniques to improve the image
quality. Ravanbod  employed fuzzy logic to neural networks to improve the
algorithm for detecting corrosion defects using ultrasonic testing technique. Ravanbod
and Jalali  presented an acquisition system for ultrasonic images and proposed a
fuzzy edge detection method. Compared with other methods, the proposed method
performed better because it has a constant minimum edge contrast. Shakibi et al. 
developed a signal processing scheme to increase the time resolution of an ultrasonic
system. Cau et al. [52,53] preprocessed UT signals with DWT, Blind Separation
techniques or FFT to be used as input for neural networks models, to classify the
information for defect detection. Chen et al.  fused the Morlet wavelet with the least
mean squares (LMS) adaptive filter to process ultrasonic signals. Iyer et al.  also
utilized both wavelet transform and neural network to process ultrasonic signals. Saniie et
al.  combined a neural network with split-spectrum processing for ultrasonic target
detection and characterization.
2.1.3 Electromagnetic acoustic transducers (EMAT)
Electromagnetic acoustic transducers (EMAT) are relatively new, and such a sensor
consists of a coil at the internal surface of the pipe wall. EMAT generates ultrasound
through Lorentz forces without requiring a coupling agent. EMAT is able to detect all
kinds of cracks, weld characteristics and wall thickness variations. The mechanism of the
EMAT inspection technique was described by Murayama et al. . Salzburger et al. 
gave a comparison between UT and EMAT and provided a brief introduction to the
development of the probe design. Tappert et al.  summarized the evolution of the
EMAT techniques from 2002 to 2007. EMAT technology is continuously being
developed in order to meet harsh environment and higher performance requirements.
Kania et al.  described the development of EMAT framework and its corresponding
validation for SCC crack inspection, and demonstrated that EMAT technology performed
very well when identifying and sizing SCC cracks. Hilvert and Beuker  gave an
introduction to high-resolution EMAT tools for analyzing cracking defects. Hirao and
Ogi  presented SH-wave EMAT technique for inspecting corrosion defects in gas
Since EMAT does not require couplant, which is its biggest advantage, it is readily
applicable in gas pipelines and the risk of overlooking defects is reduced. However,
EMAT needs to be located less than 1mm from the test body, which is too close to apply
high frequency. In addition, its detection ability and efficiency are not as good as UT.
Signal processing methods and models reported in the literature for EMAT signals are
similar to those for UT signals, since the signals are both ultrasound signals. Tucker et al.
 performed wavelet analysis to classify the EMAT signals. Mazal et al. 
compared anti-casual IIR and FIR filters, discrete wavelet transform (DWT) and wavelet
packets methods for EMAT signal processing. Through numerical tests, they drew a
conclusion that wavelet packet filtering technique performed best among these three
de-noising methods. Lee et al.  utilized wavelet transform to extract meaningful
information from EMAT signals. Kercel et al.  utilized wavelet packets and genetic
algorithm to process EMAT signals. Bolshakov et al.  investigated signal processing
methods include frequency filtering (FIR), Gaussian wavelet decomposition, synchronous
detection and their combination for analyzing EMAT data.
2.1.4 Eddy current testing (ET)
Eddy current testing is widely used in the automotive, aerospace and manufacturing
industries. As an energized coil is brought close to the surface, the impedance of the coil
is influenced by the nearness of the induced eddy current. When the eddy currents are
affected by the defects, the impedance is also altered, and this change will be measured
and used to detect defects. Eddy current testing can only be used on conductive materials.
It can detect cracks, and assess wall thickness and laminar defects. It cannot detect
external defects because of its limitation of signal through the wall. Detection of SCC
using self-excited eddy currents was introduced in . ET does not have any residual
effects like MFL, and the test is non-contact. Besides, currents induced by MFL can be
detected using ET sensors. However, at current pig speeds, the ILI applications have slow
response limits and they are sensitive to coupling variations.
2.1.5 Comparison of the four main ILI technologies
The comparison of four main ILI technologies is as shown in Table 1-1, based on the
types of defects they are applicable to. MFL or UT tools are typically used to detect metal
loss (external or internal corrosion) in pipelines. When detecting cracks (fatigue cracks
and SCC), one uses UT tools or EMAT tools. Besides, a transverse MFL tool is also
possible to be used in detecting cracks.
TABLE 1-1. Comparison of four main ILI technologies 
Y: The tool can detect this type of flaw.
N: The tool cannot detect this type of flaw.
S: Some types of the tool can detect this type of flaw while others can’t.
2.2 In-line inspection performance and applications
Understanding the performance of ILI tools is essential for applying them properly. ILI
performance is typically characterized by four measures: detection, identification,
accuracy, and locating. Detection refers to the capability that a feature is detected by the
ILI tool, and the probability of detection (POD) should be usually over 90% for ILI tools.
The detection ability of ILI tools has a significant impact on deciding inspection intervals.
Identification indicates the capability to successfully classify and report the type of
defects after being detected, and the probability of identification (POI) increases when
the size of the defect increases. The incorrect identification and classification of defects
will cause significant inaccuracy when predicting the growth of defects. The sizing
accuracy is the most significant measure to assess the performance of ILI tools, and it has
big impact on integrity management of pipelines. As the accuracy increases, the
unnecessary excavations will be reduced, selection of the essential features will be
improved and failure pressure will be well predicted. Last but not least, the capability of
accurately locating a defect can also affect maintenance and repair activities a lot.
To assess and improve in-line inspection performance, algorithms regarding sizing,
detection and classification are introduced in the literature. Hrncir et al.  gave a case
study using a proposed revised sizing algorithms, with which the confidence level of
reported feature information was improved. Caleyo et al.  presented criteria for
assessing the performance of ILI tools. There are three main types of uncertainties in ILI
tools which affect performance: systematic error of the ILI tool, measurement noise and
random error from the tool and the surface roughness . The effects of combined error
on ILI performance were studied in [71,72]. How to deal with uncertainty effects was
introduced in . McCann et al.  presented a Bayesian method to estimate the ILI
performance. Coleman and Miller  discussed normalization of data and analyzed tool
tolerance and repeatability. Elucidation of the outcomes is a big challenge when
comparing multiple ILI inspection datasets in multiple ILI tool runs . Adianto et al.
 presented the advantages for pipeline integrity program if the ILI tool performs
ILI data can be further used to assess and predict the conditions of pipelines, and
subsequently plan integrity activities. Examples of analyzing and subsequently predicting
pipeline defects (wall loss, cracks, and dents) utilizing ILI data were reported by
Anderson and Revelle , Alexander , Lockey and Young  and Ferguson .
ILI tools are widely used in the integrity management of corroded pipelines. A
comprehensive overview of in-line inspection methods for inspecting corrosion was
given in . Potential development directions were also discussed in . Methods for
assessing corrosion features, and the application of B31G and RSTRENG criteria for ILI
data, were introduced in . Lecchi  presented defect assessments of corroded
pipelines with the use of ILI tools.
Sizing cracks using ILI tools is also discussed in many papers. Bates et al.  presented
two case studies on detecting and analyzing cracks through ILI tools. Slaughter et al. 
described the procedures to analyze the ultrasonic ILI data for cracking and discussed
how to improve the crack sizing accuracy. Marr et al.  summarized the performance
of latest EMAT technology for assessing SCC. Tappert et al.  introduced in-line
inspection for all kinds of cracks utilizing EMAT based on their operational experience.
Hrncir et al.  analyzed crack sizing performance of ultrasonic ILI tools. Murayama et
al.  gave an introduction to the development of the applications of EMAT ILI. Marr
et al.  described a method to increase the probability of detection and the probability
of identification for cracks, and as a result reduce validation costs using EMAT ILI and
multiple data sets. Limon-Tapia  described a framework for managing crack defects
based on ILI tools. Nielsen et al.  compared the ILI measurements with field NDE
Overall, ILI tools have evolved a lot over past decades in the pipeline industry. Current
ILI tools perform relatively reliable in detecting and identifying different types of
anomalies. However, the sizing performance of ILI tools needs to be significantly
improved to reduce risks and costs. In addition, details of shapes of the corrosion and
crack defects need to be captured in the future, which can better assist defect growth
prediction and integrity planning. ILI tools also need to be further developed to be
suitable for various operation conditions. Signal processing methods need to be further
developed within the pipeline industry to remove noise, improve sizing accuracy and
provide better performance.
3. Pipeline defect growth prediction
There are mainly two types of methods for predicting pipeline defect growth, data-driven
methods and model-based methods. Data-based methods mainly use the ILI data or test
data to study the defect propagation stage. Applications of ILI data for defect evaluation
are discussed in the previous section, and analyzing defects through ILI data can also
give key information for predicting the growth of defects. For data-driven methods, we
will mainly discuss pipeline defect growth using ILI data, test data or sample inspection
data. Schneider et al.  predicted the defect growth and remaining useful life of
pipelines using sample inspection data. Examples on the application of artificial neural
networks (ANNs) models to predict the failure of oil pipelines were discussed by Senouci
et al.  and Lu et al. . Remaining useful life prediction for pipelines using support
vector machines (SVM) was introduced by Lee et al. , and Isa and Rajkumar .
Model-based methods mainly apply physical models such as finite element models to
perform defect prediction. For example, Liu et al.  analyzed the probability of
damage of offshore pipelines by utilizing Bayesian networks. Based on the failure
probability, pipeline remaining useful life could be predicted using physical models like
pipeline degradation models.
The pipeline defect assessment manual (PDAM) is a well-known industry project, which
gives best available methods to assess pipeline defects like corrosion, dents, etc. Cosham
and Hopkins  provided an introduction to PDAM. Cortese et al.  proposed a
calibration method for ductile damage estimation of pipelines. A variety of methods and
models are available in the literature to predict how a defect grows and when a failure
occurs. The methodologies and models used for defect growth prediction depend mainly
on the types of defects. Prediction algorithms and models for defects like metal loss,
cracking, mechanical damage, and others like third party damage are discussed
respectively in the following subsections.
3.1 Metal loss
Metal loss is a major integrity threat to oil and gas pipelines. Serious metal loss can lead
to pipeline rupture or collapse. Pipeline metal loss is mainly caused by corrosion and
erosion. The prediction methods and models regarding pipelines with corrosion and
erosion defects are discussed in this section.
Corrosion is a most common form of defects in pipelines and it can be easily affected by
the surrounding environment. Pipeline corrosion is a natural process that happens when
pipe materials interact with the working environment, such as soil and water. Corrosion
can be divided into two categories, internal and external corrosion. Nine well-known
critical environmental factors are soil resistivity, soil moisture, half-cell potential, pH,
concentrations of CO32-, HCO3-, Cl- and SO42-, and distance between the defect and the
nearest cathodic protection station . Alamilla et al.  developed a mathematical
corrosion damage propagation model considering main environmental parameters that
influence the propagation of corrosion defects. A large group of pipeline corrosion data
from 1922 to 1940 was analyzed in . A corrosion growth model can be further
generated by fitting the corrosion damage data.
A pipeline failure caused by corrosion defect can occur when either the failure pressure is
smaller than operating pressure, or the depth of defect reaches the critical threshold
(normally 80% of wall thickness in industry). The failure stress of a corrosion defect can
be expressed as a function of the size and shape of the defect and the geometry of the
pipe, as well as the material properties such as yield strength and ultimate tensile strength.
The effect of corrosion defect on burst pressure of pipelines is studied in many papers.
Netto et al.  estimated the burst pressure of pipelines with corrosion defects. The
comparison between model predictions with burst tests and long-term hydrostatic tests
was presented in .
Methods for assessing pipelines with corrosion defects have been extensively studied,
and popular code-based deterministic methods in the published literature include ASME
B31G , modified B31G , RSTRENG , SHELL92 , SAFE ,
DNV-RP-F101 (LPC) [106,107], CPS  , PCORRC [109–111]. Equations used in
these methods are similar and are based on the NG-18 equation , except PCORRC.
The differences are mainly in the defect shape factor and bulging factor in the NG-18
equation. These methods provide the prediction for corroded pipelines by determining the
burst pressure using relevant equations. Defect information such as shape and size and
pipeline physical properties such as thickness, diameter and ultimate strength are the
main factors that affect the burst pressure. Given the failure criteria, the remaining useful
life can be estimated by generating a physics-based model considering the pressure and
the defect size versus time. Modified B31G is being verified to be more accurate than
B31G, and currently it is the most popular code in the pipeline industry. Cosham et al.
 presented and compared these various code-based methods used to assess corrosion
defects. Caleyo et al.  also gave a study and comparison among some of the
above-mentioned methods. Some deterministic defect prediction models were presented
in the literature. Engelhardt et al.  predicted the growth of corrosion damage in
pipelines using several deterministic methods. Li et al.  studied correlated corrosion
defects in pipelines using modified B31G.
Monte Carlo method, first-order reliability method (FORM), and the first order Taylor
series expansion of the limit state functions are the main methods that can be combined
with deterministic methods for computing the probability of failure for a corrosion defect.
In this way, corrosion propagation model is generated and remaining useful life is
predicted. Details of these methods can be found in . Larin et al.  and Zhang et
al.  utilized Monte Carlo simulation and 3D FE models to investigate the reliability
of pipeline with corrosion defects. Teixeira et al.  utilized FORM to assess the
failure probability of corroded pipelines and this could be further used to predict the
remaining useful life of corroded pipelines.
Calculating the corrosion growth rates is an essential part of corroded pipeline integrity
management. Corrosion growth models based on corrosion growth rates are also popular
in industry. Corrosion rate can be estimated either through the physics-based corrosion
models or using ILI data. It was reported in  that the latter one gave a better estimate
for those pipelines where multiple ILI data sets are available. Race et al.  developed
a corrosion prediction model for pipelines using ILI data to determine corrosion growth
rates. However, there are typically big uncertainties when measuring corrosion growth
rates. Spencer et al.  compared successive ILI inspections for reducing the bias,
when the same ILI vendor is used or different ILI vendors are used. Bayesian method and
Markov Chain Monte Carlo (MCMC) simulation have been applied to build corrosion
growth models [123–125]. Through combining cluster technique with a Bayesian
approach, Wang et al.  proposed a methodology to estimate the real external
corrosion depth based on ILI inspection and to represent the impact of soil property
Stochastic process models were also reported to assess corrosion defect of pipelines using.
Using random process corrosion rate, researchers can develop corrosion growth models
that lead to a better fit to the data. Bazán and Beck  proposed a nonlinear random
process corrosion propagation model for pipelines. Zhou [123,127] assessed the
reliability of corroding pipelines considering the stochastic process. Valor et al. 
proposed a stochastic model for modelling pitting corrosion initiation and growth.
Alamilla and Sosa  gave a stochastic modeling of corrosion propagation based on
Other models have also been reported for corrosion growth prediction. Weiguo et al. 
proposed a method for pipeline corrosion prediction under cyclic loads. Medjo 
employed FEM calculations and Complete Gurson Model to determine the corrosion
defect development in pipelines. Das et al.  assessed turbulence models for
predicting flow-induced corrosion defects. Wang et al.  used Bayesian inference to
propose an integrated method which employed both Monte Carlo techniques and
clustered inspection data in order to assess corroded pipelines.
Sand particles are often produced along with oil and gas in the pipelines, and they can
cause erosion defects when they impact pipeline walls because of change in oil or gas
flow direction. The erosive failure of pipeline induced by sand particles is introduced in
. A detailed review of sand particle erosion modeling for pipelines was given by
Parsi et al. , where erosion prediction equations and models were discussed and
presented, and further improvements regarding erosion prediction were given.
Erosion prediction models can be categorized into Computer fluid dynamics (CFD),
experimental and mechanistic models. CFD models were widely used in predicting the
erosion damage of pipelines. CFD can be utilized to predict erosion rate and study the
impact of different parameters on the erosion rate. CFD tools are accessible but they are
simulation-based and may not be as realistic in some applications. Experimental or
empirical methods can be developed by conducting lab tests. They can provide high
quality data compared with other methods, but are generally expensive and relatively
time-consuming. Mechanistic models such as phenomenological models are analytical
ways to predict erosion defects. Although they are fast and inexpensive, the models may
be over-simplified and limited in some circumstances. Due to these limitations,
researchers proposed a number of erosion prediction models by combining these
categories. Ukpai et al.  analyzed the impact of sand particle for predicting erosion
damage using acoustic emission (AE) technique. Gnanavelu et al.  integrated CFD
with experiment results to propose a method to predict pipeline erosion. Tang et al. 
predicted the remaining useful life for a pipe with erosion under multiphase flow
condition through CFD modeling techniques. Chen et al.  proposed a CFD-DEM
coupling method to provide erosion prediction.
Cracking is a critical time-dependent threat to pipelines. There are mainly two types of
cracks, namely fatigue cracks and stress-corrosion cracking, which will be focused on in
this section. Fatigue crack propagation is defined as the process of weakening pipe
material due to pressure variation. Stress corrosion cracking, SCC in short, is the growth
of a form of environmental assisted cracks in corroded pipelines. We can divide the crack
growth process into three stages. Stage I is the crack initiation stage where the crack
growth rate is very small and can be influenced by the environment a lot. Stage II is the
stable growth stage. And stage III is the unstable crack growth stage that is less
influenced by the environment. Stage III is also called rupture to failure stage, which
happens so quickly that it is hard to control it. Researchers mainly focus on the first two
stages, with an emphasis on stage II. The fatigue life of pipeline can be defined as:
where is the number of cycles to initiate a crack, and is the number of cycles to
propagate to the failure state. We are interested in the remaining useful life, defined by
the time between the point when the defect is detected by ILI tools in stage II and the
The initiation stage of fatigue damage in pipes was studied and explained in details in
. Zheng et al.  assessed the crack initiation life if pre-deformation exists. Stage
II is the stage that researchers mainly focused on. Fatigue assessment can be obtained
based on the stress-life method (S-N), the local strain method (Ɛ-N), and Paris’ law .
The S-N method is an approach based on S-N curve, which can be obtained by fatigue
tests. The S-N approach can be employed with algorithms such as Minor’s rule, which
can be used to accumulate different stress components to further assess the remaining
useful life. So the key factors for S-N method are to determine or select S-N curves
accurately, to apply a correction factor and to use a suitable algorithm to combine all the
stress contributions. Methods utilized to predict the remaining life of the damaged
pipelines based on S-N curves were presented by Pinheiro and Pasqualino  and
Hong et al. . However, there are some limitations associated with the fatigue S-N
approach. It fails to recognize the probabilistic nature of fatigue, and it does not consider
the influence of the compressive residual stress resulting from high stress. The Ɛ-N
method is another method for fatigue growth assessment, which utilizes ΔƐ-N curves,
where N is a function of strain range ΔƐ, and ΔƐ means the total amplitude of strain
variations. This method is also similar to S-N approach in some way.
The most popular methods for crack growth models are based on the Paris’s law :
where , with being the maximum stress intensity factor
(SIF), and being the minimum SIF.
is the fatigue crack growth rate, where a is
the crack length and N is the number of fatigue cycles. C and m are two material
dependent model parameters. There are three primary modes of fracture. Mode I is called
opening mode, mode II is sliding mode and mode III is tearing mode. As a result, SIF
should also reflect these three modes. Mode I SIF (KI) dominates the magnitude of crack
propagation, and many papers only calculate KI to represent the total stress intensity
while using Paris’ law. In the literature, the majority of physics-based models for
predicting cracks in pipelines are based on the Paris’ law. To employ the Paris’ law, one
needs to determine the SIF first. SIF can be determined through standard codes,
numerical equations derived by researchers, experiment results and finite element
software (ANSYS, ABAQUS, etc.). There are different equations to calculate the SIF for
different crack types. The categories of crack shapes in pipelines are surface, embedded,
and through thickness cracks. Shim and Wilkowski  applied FE simulation to
calculate bulging factor for a pipeline with cracks in the external surface. The bulging
factor could be further utilized to determine SIF and crack-driving force. Beside SIF,
other measures such as crack tip opening displacement (CTOD) and crack tip opening
angle (CTOA) can also be used to determine the fracture toughness of most materials
including pipeline materials. Ben Amara et al.  gave a study on how to obtain
CTOA in steel pipelines.
Popular standards for assessing crack defects include API 579 , BS 7910  and
NG 18 , and many pipeline companies follow these standards to make decisions.
Software tools such as CorLAS  are also used to analyze these defects. Some other
physics-based approaches are also introduced in the literature. Popelar et al. 
developed a theoretical model to simulate and calculate the propagation speed of crack in
pipelines. Pipeline crack prediction with strain rate dependent damage model (SRDD)
through experiments and simulation were investigated by Oikonomidis et al. [150,151],
and Yu and Ru . Iranpour and Taheri   did research on the impact of
compressive stress cycles and peak tensile overload cycles on the fatigue life of pipelines.
Amaro et al. [155,156] proposed a hydrogen-assisted fatigue crack propagation model,
which is used to predict crack growth using a function of and hydrogen pressure.
Besides, Sekhar  summarized the effects as well as the identifications of the
multiple cracks, and more studied were needed to consider multiple cracks in pipeline
crack growth prediction. Polasik and Jaske  described a crack growth model based
on the Paris’ law and fracture mechanics principles. Hadjoui et al.  studied the
behavior of crack growth of double butt weld in two pipeline material, X60 and X70.
Nonn and Kalwa  analyzed multiple published ductile damage mechanics models
including Gurson-Tvergaard-Needelman (GTN), Fracture Locus Curve (FLC) and
Cohesive Zone (CZ)) for ductile crack propagation in pipelines.
Experimental testing of pipelines with crack defects was performed and reported by many
groups. Kumar et al.  used acoustic emission (AE) method to study the behavior of
crack propagation in low carbon steels which can be used as the pipeline material. Slifka
et al.  gave tests on two pipeline steel to get fatigue crack growth rate. Jin et al. 
performed a test on pipeline steel to assess the propagation of a semi-elliptical surface
crack. Hosseini et al.  compared the experimental testing results they obtained with
the industrially known methods, such as BS 7910 and NG 18. Pumpyanskyi et al. 
performed full scale tests to look into crack propagation and arrest behavior of pipelines.
Chen and Jiang  gave experimental investigations on crack growth analysis of
pipeline material X60. Naniwadekar et al.  predicted flaw growth in various
orientations based on frequency measurements.
Physics-based models may not be applicable to all situations due to complexity of the
applications and availability of authentic models, and the challenge in determining model
parameters. ILI tools are very expensive to run, and sometimes there are not sufficient
data to effectively run data-driven methods. As a result, there are great room and
challenges for improving prognosis methods and models for cracks in pipelines. Hybrid
methods are also being investigated, which integrate physics-based models with
data-driven methods. An integrated prognosis method for industrial and mechanical
structures was introduced by An et al.  using Bayesian inference.
A corrosive environment can affect the growth of fatigue crack . We can call this type
of crack environmental cracking or SCC. A probabilistic damage model was proposed by
Hu et al.  to assess local corrosion crack based on Monte Carlo simulation. Lu et al.
 presented a high pH stress corrosion crack growth model and validated it through
experiment. Imanian and Modarres  presented an entropy-based method and did
experiments to assess the reliability for corrosion fatigue. Chookah et al.  proposed
a physics-of-failure model for predicting the propagation of SCC. Jaske and Beavers 
used the available data and employed J-integral fracture mechanism to predict pipeline
remaining life subject to SCC.
3.3 Mechanical damage
Mechanical damage on pipelines also poses threats to pipeline integrity. Two main
categories of mechanical damage are dents and gouges. Bai and Bai  gave an
introduction to dented pipes including limit-state based criteria, fracture mechanism and
reliability-based assessment. A mechanical damage integrity management framework
was given in . The burst pressure for pipelines with gouges and dents was studied by
Lancaster and Palmer , Allouti et al.  and Ghaednia et al. . Pressure
strength of pipelines with dents and cracks were studied in . Macdonald and
Cosham  discussed the pipeline defect assessment manual (PDAM) and suggested
practices for dents and gouges assessment as well as the limitations of these assessment
methods. Cosham and Hopkins  analyzed the dents effect in pipelines based on
Prognosis algorithms and models are proposed for mechanical damage in pipelines.
Ivanov et al.  proposed an FE model using MFL signals to predict the growth of
mechanical damage in pipelines. Bolton et al.  proposed a finite element model for
predicting the life for dented pipeline and validated the model by experiment. Dama et al.
 used a simple S-N approach to assess the structural condition of pipelines with
sharp dents. Bolton et al.  developed a finite element model for dented pipes to
estimate the remaining life. Azadeh and Taheri  performed an experimental
investigation on dented pipes. Failure prediction of the pipeline with dents based on local
strain criteria was studied by Allouti et al.  and Noronha et al. .
3.4 Other defects
Other types of defects, such as weld, third party damage, etc., can cause the failure of
pipelines. The main differences between corrosion, cracking and the other failure
mechanisms (third-party damage, laminations and earth movements) are the nature of
mechanism and failure rate tendency. The nature of mechanisms of corrosion and
cracking are time-dependent while the others are generally random. The failure
tendencies for corrosion and cracking increase with time, while those for the others
remain constant. To better control third party damage, regular surveys of the line, good
communications, and good protective measures are important. Goodfellow et al. 
presented the updated distributions of third party damage with the use of historical data.
Hsu et al.  provided an introduction to weld mechanism and introduced wear
prediction models for metals. El-Hussein  compared the FE predictions for third
party attacks with real field data. Oddy and McDill  employed 3D FE analysis of
welding on pipelines to perform predictions. Niu et al.  applied FE simulation to
give a creep damage prediction of pipelines in the high temperature and high pressure
4. Risk-based management
The common definition of risk is the multiplication of probability and consequence. Thus,
to perform risk-based management, we need to analyze the causes of risks, estimate
failure probabilities as well as perform consequence analysis. For pipeline integrity
management, probabilities typically refer to probabilities of pipeline failure due to certain
defect growth. The consequences are related to the costs incurred by activities like
inspection and maintenance, loss of productivity, rehabilitation and investigation, damage
to the environment and community, environmental cleaning up, etc.
Activities for risk mitigation include visual inspection, potential surveys,
cathodic-protection inspection, in-line inspection, operational pigging and other
maintenance and repair activities. Emergency plans for failure and accidents also need to
be considered. The frequency of inspection and maintenance activities depends not only
on the defect damage situation and the consequences of failure, but also on the pipeline
operation conditions. Besides, risk acceptance criteria need to be determined before
risk-based management process, based on industry regulations and codes, operators as
well as risk analysis outcomes. Pipeline risk analysis for integrity management was
introduced in [192–195]. The advantages and disadvantages of pipeline risk analysis were
discussed by Bott and Sporns .
Analyzing the reliability and risks is the essential job in the preparation stage for
risk-based management. Many papers discussed the inputs for the risk-based
management. Chien and Chen  carried out the reliability assessment of pipelines to
provide the integrity management strategies, and the reliability analysis method they used
was first order second moment (FOSM) method. Kuznetsov et al.  implemented
Bayesian method to count the amount of defects in a pipeline segment, and it can be
further utilized in determining the inspection and maintenance activities. Cunha 
compared and analyzed the failure statistics for pipelines which can also be further
utilized as the basis for risk-based management. McCallum et al.  developed a
corrosion risk management tool using Markov analysis, which can assist corrosion
integrity planning. Mihell and Rout  proposed an approach to analyze risk and
reliability for pipelines. Tuft et al.  provided the comparison between
reliability-based analysis method and quantitative risk assessment based on historical
There are two main objectives in risk-based management models. One objective is to
minimize the whole life cycle cost with the constraints of certain reliability and risk level,
and the other objective is to minimize the risk. Various approaches and models were
reported with cost minimization as the main objective. Dawotola et al.  proposed a
method where the failure rate changes with time following a nonhomogeneous Poisson
process. The historical data was fitted to obtain the probability of failure, and
maintenance strategies were optimized by minimizing operation and maintenance loss
while meeting risk and reliability target. Bai et al.  proposed a tree risk-based
inspection approach for subsea pipelines to minimize cost for different safety levels.
Sinha and McKim  utilized Markovian prediction models to construct a
cost-effectiveness based prioritization program to develop strategies for maintenance and
repair. Life cycle cost optimization was performed using Genetic Algorithm (GA) for
pipeline networks by Tee et al. . In addition, inspection, maintenance and repair
strategies for different types of defects in pipelines were also reported. Sahraoui et al.
 provided a review of risk-based management methods that considered the
uncertainties in the inspection results for pipelines with corrosion defects. Stephens et al.
 studied reliability corrosion assessment to develop cost-effective maintenance and
inspection planning strategies, and they adopted an random process model to generate
new defects when calculating the probability of failure. Hong  developed inspection
and maintenance schedules based on reliability constraint for corroded pipelines. Moreno
et al.  extended the inspection interval using a statistically active corrosion (SAC)
method. Gomes et al.  optimized the inspection planning and repair intervals for
pipelines with external corrosion defects. Gomes and Beck  also optimized pipeline
management subject to random cracks. The number of inspections and critical crack size
were considered to be design variables in the models.
The second optimization objective used in many studies is risk minimization, mainly
aiming to reduce the likelihood (probability of failure) and/or the consequence (severity).
Kamsu-Foguem  presented an introduction to risk-based inspection management,
and suggested a methodology based on a colored risk matrix for providing risk
acceptance criteria. Tien et al.  proposed a method to determine the optimal pigging
inspections planning, with information like damage factor, inspection factor, condition
factor, process factor, etc., collected and qualified to form the model built in this paper.
Khan et al.  proposed a method for risk-based inspection and maintenance modeling
using gamma distribution and Bayesian method to describe material degradation process.
Kallen and van Noortwijk  proposed an adaptive Bayesian model for optimal
integrity planning, which used gamma stochastic process to describe the degradation
Many studies on maintenance planning were reported for pipelines with a specific defect
type, particularly corrosion defect. Singh and Markeset  proposed a method to
estimate corrosion growth rate for pipelines based on fuzzy logic method. A decision
support system (DSS) was utilized for assessing risk effects and developing pipeline
integrity plans in  and . Condition based maintenance models developed for
multi-component systems were introduced in [219,220]. Seo et al.  discussed the
development and application of the proposed risk-based inspection method for pipelines
with corrosion defects. Fessler and Rapp  proposed a method for determining the
reassessment intervals for pipelines with SCC defects. Zarea et al.  gave an
introduction to risk management along with integrity management of mechanical damage
Integrity has been the top priority for the pipeline industry, and plays a critical role for the
oil and gas industry as a whole. Significant advances are needed in pipeline integrity
management to develop more effective methods, models and technologies to accurately
monitor and predict pipeline conditions, extend the lifetimes of pipelines and prevent
potential ruptures and the resulting consequences. In this paper, three main steps of a
pipeline integrity program have been discussed. Key ILI techniques along with their
performance and applications have been reviewed. Data-driven methods and
physics-based model for predicting pipeline defect growth have been discussed in details.
Risk-based inspection and maintenance methods and models have also been presented.
In-line inspection, defect prediction and risk-based planning, which are three main steps
of pipeline integrity management, actually form a closed loop. Plan, schedule, execute,
analyze and improve are the elements of the loop of the activities need to be performed to
manage pipeline integrity.
In addition to the research studies reviewed in this paper, fundamentals of pipeline
integrity management were covered in several books. Mohitpour , Singh , and
Revie  provided introductions to basic concepts and assessment actions to assure
pipeline integrity. Timashev and Bushinskaya  presented an introduction to
prognosis and reliability of pipeline systems, where ILI results analysis, reliability
analysis and methods for assessing pipeline system were covered. Muhlbauer [218,226]
introduced methods for managing pipeline risks. Bai  presented an introduction to
risk management of different categories of threats in pipelines. ILI technologies and
applications were discussed in . The regulations of pipeline safety were presented
for Great Britain in .
Today still about half of pipelines are non-piggable, where smart pigs and ILI can not be
employed, and challenges exist when gathering valuable data utilizing direct assessment
methods for defect growth prediction. As to piggable pipelines, although ILI technologies
are continuously being improved in a fast pace, the measurement errors of ILI tools can
still cause large uncertainties when evaluating defects and predicting defect growth. Each
individual pipeline has its specific situations, and it’s hard to develop physics-based
models that considers all these specific factors. In this case, both data-driven methods and
physics-based models for pipeline defect growth prediction may not be accurate enough
and may lead to poorly managed integrity activities. Multiple ILI tool runs can give more
accurate results but cost more. Many reported approaches and models in the literature
were not thoroughly validated. More effective communications are needed between
researchers and the industry.
To address these problems, the following research directions need to be further
investigated in pipeline integrity management. In-line inspection sensor technologies and
pipeline integrity practices must continue to evolve. Other inspection technologies need
to improve too for non-piggable pipelines. More reliable and effective signal processing
and data analysis methods need to be developed for noise removal in ILI data and
accurate defect evaluation. Prognostics approaches and models need to be further
improved. Minor repair and imperfect repair actions need to be considered and compared
with other maintenance actions. Balancing the ILI tool run times with costs also need to
be further investigated. Different pipeline integrity management frameworks need to be
further developed regarding different types of defects. Effective validation methods and
technologies also need to be established.
This research was supported by the Natural Sciences and Engineering Research Council
of Canada (NSERC) and the University of Alberta.
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