Risk-based Inspection Planning of Oil and Gas Pipes – The Fuzzy Logic Framework
a report by Maneesh Singh,1,2 Thom Fosselie1 and Frode Wiggen1 1. Det Norske Veritas (DNV), Stavanger; 2. Department of Engineering and Safety, University of Tromsø
Risk-based inspection (RBI) methodology is commonly used in the offshore industry as a component in the development of inspection and maintenance programmes. It is used to judiciously divide resources among different assets based on the perceived risk of failure, where the risk is defined as a combination of the probability of failure and its consequences.1
The development of an RBI programme is carried out in a complex environment where a number of inter-related factors come into play. Thus, the maintenance management programme requires an integrated plant-wide information system incorporating management, scheduling, planning, optimisation, supervision, fault detection, fault diagnosis, logistics, etc.
The risk analysis can be carried out qualitatively or quantitatively. In a qualitative risk analysis the risk is estimated based on simple calculations and expert opinion; by contrast, in quantitative risk analysis various components such as the probability of failure and its consequences are calculated based on inspection and modelling results. Unfortunately, the quantitative calculation of risk is a daunting task because of the difficulties involved in calculating the probability of failure and the effect of the consequence of failure. Thus, the companies often use a combination of the two, called semi-qualitative or semi-quantitative (semi-Q) approach, as a good balance between precision and practicality.1
The quantitative approach requires a number of models – semi-empirical and mechanistic – based on the understanding of natural phenomena such as degradation, failure, explosion and pollution. Unfortunately, the complex nature of these phenomena does not make it amenable to modelling. As a result, most of the models have been successful in making accurate predictions only to a limited degree. Luckily, while developing a RBI programme, the maintenance engineers provide sufficient safety margins. Hence, it is not vital to accurately model all of the processes over a wide range of conditions. Instead, the requirement is for a simple yet robust tool that would be able to give rough estimates.
An important feature of plant operation is the availability of a considerable amount of information as qualitative and imprecise knowledge. This subjective expert knowledge cannot be easily used by the traditional mathematics based on differential and algebraic equations. Hence, there is a requirement for a technique that can incorporate the subjective knowledge along with the objective information to develop a practical model that is simple to use, flexible enough to be modified according to the requirements of different sections of the plant and able to incorporate field data.
Fuzzy logic is one of the promising techniques that have been developed to handle uncertain, imprecise and subjective knowledge. It is an attractive option when the available data are not precise enough
© TOUCH BRIEFINGS 2010
to allow conventional methods of computing, it is sufficient to have an approximate solution allowing for the development of a simple and robust model, the available information is too incomplete to allow the development of a proper model and the model is too difficult to allow easy computation.2,3
This article presents an outline of the ongoing project on the development of a fuzzy-logic-based methodology for inspection scheduling.
Use of Fuzzy Logic for Establishment of Risk-based Inspection
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. This rule-based model allows for the integration of numerical components of the model with subjective expert knowledge.2–4
The benefits of using fuzzy logic for establishing an inspection schedule include the following:2–4
• Fuzzy logic has the ability to handle imprecise and vague knowledge about the phenomenon. It is also able to use the expert knowledge available to maintenance engineers, plant operators and designers.
• Fuzzy logic models can help the human operators take a more consistent, transparent and reproducible decision by emulating human reasoning. The model achieves this by translating their a priori knowledge into a knowledge-based system. This helps to trace back the decisions taken based on the observed results.
• Inspection or maintenance scheduling does not depend on the precise results; it suffices to give a period instead of the exact date for an activity.
• The calculation of rate of degradation is only one part of the whole decision-making process in developing the inspection schedule. Fuzzy logic can help in developing an ordered decision-making sequence incorporating various quantitative and qualitative factors governing the inspection scheduling.
Methodology for the Risk-based Inspection Figure 1 shows a simplified flow chart for developing the RBI schedule for oil and gas topside equipment. Fuzzy logic models can be developed for each of the modules.
This article discusses the development of the consequence of failure module. Figure 2 illustrates a simplified consequence of failure assessment process. In the case of loss of containment, the consequence can be determined using:
• the Fire Model – to calculate the possibility of fire/explosion and its consequences;
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Subsea & Pipelines
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