Risk-based Inspection Planning of Oil and Gas Pipes – The Fuzzy Logic Framework Figure 1: Simplified Risk-based Inspection Assessment Working Process1 Risk analysis planning
other operational domains, such as managerial, operational and financial, as long as they are constructed in a compatible format. Thus, by maintaining a generic structure, the fuzzy logic can incorporate diverse activities to form a well co-ordinated solution.
Hazard identification
Development of a Model Based on Fuzzy Logic Fuzzification of the Input Variables
PoF CoF Risk analysis Risk acceptance criteria Risk evaluation Low Medium/high
Planned inspection Planned preventative maintenance
CoF = consequence of failure; PoF = probability of failure.
• the Toxicity Model – to calculate the consequence due to the release of toxic chemicals. This also accounts for the release of non-toxic gases such as nitrogen in a closed environment; and
• the Physical State Model – to calculate the effect of physical properties such as temperature and pressure of the released fluid.
Figure 3 illustrates a more detailed assessment of the Consequence of Failure – Safety using the Fire Model. In the event of the release of fluid, the parameters that will affect the consequence are:
• age of the installation; • safety condition of the installation; • maintenance condition of the equipment; • amount of leakage; • characteristics of the leaking fluid; and • manning in the region affected.
Some of these parameters can be determined numerically, while others have to rely on the personal judgement of the experts. These
A fuzzy-logic-based model is a logical model that uses a set of ‘if–then’ rules and logical operators to establish a relationship between the input variables and the outputs.
parameters will influence the possibility of fire/explosion and the area affected in the event of fire/explosion. The size of fire/explosion will, in turn, affect the safety, economy and environment, giving the consequence of failure for the three.
Along the lines of this module, some other modules have also been developed. This modular design can also be further extended to include
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Minimum inspection Planned corrective maintenance
Out of all the variables that affect the rate of corrosion, the variables that are more important are judiciously selected and described as the linguistic variables. This short listing helps to optimise the accuracy and complexity of the system by incorporating the important variables and leaving out the less important ones. Too many variables will increase the complexity of the model, hence adding to the noise in the calculated results, and too few variables will result in erroneous results due to the disregard of important parameters.3–5
Each of the linguistic variables (e.g. installation_age, safety_condition, ignition_likelihood, consequence of failure [CoF], etc.) has a number of linguistic terms or fuzzy variables (e.g. new, medium, old, etc.). As the number of fuzzy variables in a linguistic variable increases, the accuracy of the model also increases, but so does the complexity of the model because it entails an increase in the number of rules in the rule base.3–5
The next step is the determination of the membership function µ(x) connecting the value of input to the degree of compatibility or truth within a fuzzy variable. While developing the membership functions, a number of factors are taken into account such as the extreme values between which the values oscillate, expert opinion of the maintenance engineers, etc.
The membership functions of the variables can be adapted to a number of shapes such as sigmoidal, Gaussian, bell, etc. In this work, the triangular and trapezoidal fuzzy sets have been mostly used because they were the most common, are easy to use and adequately reflect most of the processes.3–5
Creation of Linguistic Rule Base
The linguistic rule base contains the empirical knowledge used by the model. It comprises of a set of linguistic if–then rules. In this work, the rule base is a Multiple Input Single Output (MISO) system consisting of a number of rules linking many inputs with a single output.3–5
The linguistic rule comprises linguistic statements connected by ‘and’ and a conclusion. A linguistic statement consists of a simple basic structure: ‘linguistic variable – symbol of comparison – linguistic term’. An example of a linguistic statement is ‘installation_age is new_installation_age’. Thus, a rule is of the form:3–5
Rule Ri: ‘if’ (linguistic statement or antecedent proposition Pi1) ‘and’ (linguistic statement or antecedent proposition Pi2) ‘then’ (conclusion or consequent proposition Ci).
For example:
Rule 1: ‘If’ installation_age is new_installation_age ‘And’ safety_condition is good_safety_condition
EXPLORATION & PRODUCTION – VOLUME 8 ISSUE 2
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