This article describes a computerized model called the Corrosion Assessment Algorithm (CAA) that was developed to assess corrosion risk in pipelines. The CAA software incorporates factors related to microbiologically influenced corrosion and can distinguish risk of corrosion from cost of corrosion. It uses a questionnaire approach to input data on the pipeline system and subsystems. The software then calculates corrosion risk percentages and compares how risk may change with modifications to the pipeline. Two case studies are presented that demonstrate how the CAA software can evaluate risk levels for internal and external pipeline systems and compare the effects of different mitigation measures.

1. 56 MATERIALS PERFORMANCE January 2005
A Computerized
Model Incorporating
MIC Factors to
Assess Corrosion in
Pipelines
R. JAVAHERDASHTI and E.G. MARHAMATI, Monash University
This article presents a computerized model to
assess pipeline corrosion. The software was
developed by the authors and can take microbiologically
influenced corrosion-related factors into consideration.
The other important feature of the software is that it can
distinguish the “risk of corrosion” from the “cost of
corrosion.”
A
n effective way of assessing
corrosion in pipelines be-
fore serious consequences
occur is to use tools that can
predict the future of a pipe-
line through an understand-
ing of its present state. A
valuable tool in this regard is the use of
software that is capable of drawing a
picture of the pipeline through the ap-
plication of some algorithms.
Intrinsically there are some drawbacks
associated with such a process. Some of
these drawbacks are given below.
• The domain of variables that govern
the present and future states of a pipe-
line is too wide; it contains many
known factors with no clear idea about
how they interact, as well as additional
unknown factors. This domain of vari-
ables introduces certain error margins
that may have had adverse effects on the
implementation of the principal math-
ematical model upon which the algo-
rithm is based.
• Although to some extent the present
state of a pipeline’s mechanical, physi-
cal, and chemical characteristics may be
known, no guarantee about its future
can be given. For instance, because of
political and geographical reasons many
oil and gas pipelines may have been
targets of sabotage. If a branch of a
pipeline is attacked, the operator has to
overload the other branches in order for
operations to continue. Therefore, the
operational limits may be changed so
drastically that a realistic estimation of
the pipeline’s lifetime becomes impos-
sible.
An incident of this sort happened in
Iraq before the overthrow of Saddam
Hussein’s regime. In many Iraqi oil pipe-
lines the oil had been idle for a long time
so there could have been serious doubts
about the pipeline’s capability for rou-
tine operation. For this reason, the soft-
ware model must rely on the general
characteristics of the pipeline and its
operation rather than on specific char-
acteristics, so that even in the course of
an unpredictable event, the model
would still be usable. Horvath has re-
viewed the role of a corrosion engineer
in risk assessment activities and the way
that software modeling may be useful.1
• The algorithms or the data-entering
procedure may be so long or unfriendly
to users that any practical use of the
software would be difficult to inter-
pret.
Bearing such drawbacks in mind, an
algorithm has been developed by one of the
authors that relies mainly on factors that
are of known practical importance in the
pipeline industry. These include factors
that are both mechanically and chemically
important. The algorithm uses a well-
known method to assess risk in industrial
JAN 2005 MP pp 52-96.indd 56
JAN 2005 MP pp 52-96.indd 56 12/13/04 8:00:27 AM
12/13/04 8:00:27 AM

2. January 2005 MATERIALS PERFORMANCE 57
environments, known as the “Frank &
Morgan method.”2-3
This method is used
mainly to supply industrial managers with
the knowledge necessary to allocate the
required capital at the lowest level of risk.
This method has the following primary
characteristics when it is applied to differ-
ent departments of a factory:
• Risk index for each department
• Relative risk index for each depart-
ment
• Evaluation of the capital that is ex-
posed to danger in each department
• Total risk evaluation for each depart-
ment
• Ranking of the departments accord-
ing to each department’s total num-
ber.
Corrosion Assessment
Algorithm for a Buried Pipe
An advantage of the Frank & Morgan
method is that it evaluates different as-
pects of risk to offer an idea of the severity
of the risk already present in the factory
and its relevant department(s). Following
the general framework of the Frank &
Morgan method, an algorithm was de-
signed to evaluate the risk of corrosion.
This algorithm, called the “Corrosion As-
sessment Algorithm” (CAA), was adjusted
by the authors for a buried metallic pipe
that is coated and protected by cathodic
protection (CP).
A very important characteristic of this
method is that it puts appreciable weight
on microbiologically influenced corrosion
(MIC) and its contribution to pipeline
corrosion in general. The algorithm devel-
oped for the software has many flexibili-
ties. One of these is the consideration of
factors that may severely contribute to
corrosion (danger list) and the counter-
measures against corrosion that may have
been introduced (control list), either dur-
ing pipeline operations or after the pipe-
line has been subjected to mitigation
programs.
In a buried, coated metallic pipe, the
following corrosion systems are defined
according to corrosion management
principles:4-5
• System: the pipe
• Subsystems: the coating; the fluid
within the pipe that could consist of
water, gas, oil, etc.; and the soil around
the pipe.
The subsystems can be categorized
further as 1) internal and 2) external sub-
systems; moreover, the fluid and the lining
constitute internal subsystems whereas
the soil and coating form external sub-
systems (Figure 1). The value of the sys-
tem and subsystem definitions is that they
define the characteristics of the pipe as
completely as possible. More details of the
CAA algorithm and the software have
been given elsewhere.6-7
How the Software Works
In its present form, the CAA software
uses a questionnaire approach. In each
window, the user is asked to fill out a short
form that contains some data regarding
the present state of the pipeline. Figure 2
presents one such window.
A certain weight is attributed to each
question, according to its importance.
Questions with unknown answers can be
Main corrosion systems and subsystems in a pipe.
A sample window showing the type of information required (screen printout courtesy of
R. Javaherdashti).
FIGURE 1
FIGURE 2
JAN 2005 MP pp 52-96.indd 57
JAN 2005 MP pp 52-96.indd 57 12/13/04 8:00:36 AM
12/13/04 8:00:36 AM

3. 58 MATERIALS PERFORMANCE January 2005
left blank, in which case a weight of 0 is
attributed to it. One of the main advan-
tages of the CAA software is that the user
can leave blank any question whose answer
is not known and still come up with a cor-
rosion risk. When the answer to an unan-
swered question is known, the user can go
back to the file and refresh it by answering
more questions. The result will, in turn,
be a different level of corrosion risk.
The software is format-based and writ-
ten in the C++
language. The software re-
views nearly all known factors that can
contribute to corrosion in pipelines, in-
cluding factors that cause MIC. The soft-
ware consists of six windows with easy-to-
follow questions. The user can not only
calculate the “risk of corrosion” but also
the “cost of corrosion” and then decide
whether corrosion treatment of the pipe
will be economical in the particular case
he is studying.
Examples
Table 1 shows two cases that a com-
pany used to evaluate the risk and cost of
corrosion. Figures 3 and 4 show the resul-
tant outputs.
In Case 1, although the risk of corro-
sion for the external system is high
(~51.6%, Figure 3), due to the higher
possibility of expenses for the internal
system, the internal system is picked as
the system with higher financial risk.
However, the system itself possesses both
the lowest corrosion risk and financial
risk. In Case 2, the measures taken have
resulted in decreasing internal corrosion
risk from 48.4% to 43.7% (Figure 4),
whereas the corrosion risk of the external
system has increased slightly (from 51.6%
in Case 1 to 56.3% in Case 2). This in-
crease can be interpreted as the corrosion
risk associated with factors such as detec-
tion of corrosion-enhancing bacteria
other than sulfate-reducing and sulfur-
oxidizing around the pipe in Case 2
(Table 1).
These examples show that the CAA
algorithm has the capacity of not only
marking which system needs more atten-
tion to render it comparatively safer from
corrosion, but it is also capable of compar-
ing the modifications that can be done on
a pipe and its internal or external environ-
ment.
Conclusions
• Although in Case 2 the financial risk
of the internal corrosion system has
been reported low in comparison with
the external system corrrosion risk, it
is actually much lower than the finan-
cial risk of the internal corrosion sys-
tem as given in Case 1. This implies
that any improvement in corrosion
resistance of the pipe can have impor-
tant advantages on reducing expendi-
tures.
• The above examples show that the
CAA algorithm has the capacity of not
only marking which system needs
more attention to render it compara-
tively safer from corrosion, but also it
is capable of comparing the modifica-
tions that can be done on the pipe and
its internal or external environment.
• CAA also indicates that the more
that is known about the conditions of
a component, such as the characteris-
tics of the fluid inside the pipe, the
TABLE 1
PIPELINE CASES USING CAA SOFTWARE
Case 1 Case 2
Pipe material: available “cheap” steel Same pipe as in Case 1
Pipe carries oil with high moisture content It has lining (polyurethane)
Pipe is under CP Pipe material has been upgraded to suitable steel
The fluid (oil + water in the pipe) is almost stagnant Pipe is now lying above the soil water table level
Pipe has polyvinyl chloride coating with no lining By aid of pumps and changing design, the fluid now
Pipe lies below soil average water table is moving with a speed >1.5 m/s
Soil around the pipe is mainly clay Hydrotest has been carried out in a correct, complete
Hydrotest is incorrectly carried out way
Inhibitor/biocide is used in water phase of the fluid Welding and post-welding treatments have been
thoroughly done
Microbial studies have shown that main micro-
organisms of the soil around the pipe are
other than sulfate-reducing and sulfur-oxidizing
bacteria
Final output of CAA software, Case 1 (screen printout courtesy of R. Javaherdashti).
FIGURE 3
JAN 2005 MP pp 52-96.indd 58
JAN 2005 MP pp 52-96.indd 58 12/13/04 8:00:45 AM
12/13/04 8:00:45 AM

4. January 2005 MATERIALS PERFORMANCE 59
greater the likelihood that one can
make better decisions about both
maintenance and mitigation pro-
grams.
• In the algorithm, the weights given
to the items may be altered. Even the
number of conditions for each corro-
sion system may increase (or decrease).
The algorithm and its framework will
not change, however, so that it can be
used for any corroding system using
corrosion management. Applying
CAA also suggests that the more that
is known about the condition of the
subsystems, the more effectively one
can coordinate an efficient design and
application of both mitigation and
maintenance programs.
• The CAA algorithm is the only al-
gorithm of its kind known to the au-
thors that includes information on
MIC and calculates its contribution to
corrosion.
References
1. R.J. Horvath, “The Role of the Corrosion Engi-
neer in the Development and Application of Risk-based
Inspection for Plant Equipment,” MP 37, 7 (Houston,
TX: NACE, 1998).
2. R. Braure, Safety and Health for Engineers (New
York, NY: Van Nostrand Reinhold, 1990).
3. B. Nichols, System Safety and Risk Assessment
(Oxfordshire, U.K.: Taylor & Francis, 1997).
4. R. Javaherdashti, “Managing Corrosion by Cor-
rosion Management: A Guide to Industry Managers,”
Corrosion Reviews 21, 4 (2003).
5. R. Javaherdashti, “How to Manage Corrosion
Control without a Corrosion Background,” MP 41, 3
(Houston, TX: NACE, 2002).
6. R. Javaherdashti, “Assessment for Buried, Coated
Metallic Pipe Lines with Cathodic Protection: Propos-
ing an Algorithm,” CORROSION/2003 (Houston, TX:
NACE, 2003).
7. http//www.parscorrosion.com.
R. JAVAHERDASHTI is with the Department of
Materials Engineering, Monash University, VIC
3800, Australia. He is a former member of the
board and secretary of the Iranian Corrosion
Association. He has authored three books and
more than 30 papers on MIC and corrosion
management. He is a member of the ASM journal
Materials Engineering international review board
and is listed in the 8th edition (2005-2006) of
Marquis Who’s Who in Science and Engineering.
E.G. MARHAMATI is a Professional Software
Engineer with the Department of Electrical and
Computer Systems Engineering at Monash
University.
Final output of CAA software, Case 2 (screen printout courtesy of R. Javaherdashti).
FIGURE 4
JAN 2005 MP pp 52-96.indd 59
JAN 2005 MP pp 52-96.indd 59 12/13/04 8:00:53 AM
12/13/04 8:00:53 AM