Risk-based design of oil and gas pipelines, risers.
accidental or catastrophic failures.
A traditional integrity assessment can be a time-consuming, complex and expensive process. However, incorporating a risk-based design (RBD) is an alternative method to protect a pipeline against corrosion dependent defects or cracks. The probability of accidental or catastrophic failure can be minimized or avoided by considering future risk sources in the early stage of the design.
The risk-based design methodology is framed in the flow chart (Figure 1).
Defining Load And Resistance
The load and resistance of a pipeline may be defined by considering the limit state design (LSD) or load and resistance-factored design (LRFD) as given in Figure 2. In limit state design, the factored load and resistance are obtained by multiplying characteristic load and resistance values by safety factors.
[FIGURE 1 OMITTED]
[FIGURE 2 OMITTED]
The class of pipeline may also be considered to account for the severity of the consequences associated with the failure. This approach is semi-probabilistic in that it requires defining load and resistance in terms of distribution, mean and standard deviation.
Assessment of failure probability (Pf): The distribution of stresses, strains or any other kind of displacements in the pipeline should be determined from principles of statics, dynamics or kinematics. The selection of performance model (elastic or plastic analysis) should be based on specified material properties and material behaviors.
The probability of failure is determined by considering the load and resistance acting on the defective or cracked pipeline. It is expected that defects or cracks will be created in the pipeline, assuming a specific growth rate for different kinds of corrosion within the stipulated design life.
Consequence analysis (Cf): This assesses the severity of adverse effects of accident on people, property, environment or a combination of these. Consequence analyses predict the magnitude of the effects resulting from release of toxic or flammable fluids and disruption of pipeline throughput.
Risk estimation: This combines the results of frequency and consequence analysis to produce a measure of risk. The risk associated with a pipeline failure can be expressed as:
Risk = [P.sub.f] x [C.sub.f]
where Pf is the failure probability, and Cf is a measure of the consequences of failure.
Component Risk Evaluation
Risk analysis provides the most consistent and rational basis for selecting target reliability levels, based on criteria such as economic optimization, safety and environmental protection. On the basis of probability of failure and consequence analyses, the target reliability level of the pipeline can be selected either to meet a predetermined acceptable risk level or to optimize the total cost considering the cost of failure.
If the estimated Risk = [P.sub.f] x [C.sub.f] is not within acceptable risk limits, the pipeline parameters (D, t, Corrosion Susceptibility, and a for pipeline) may be revised to meet the target safety level.
To this point, the methodology calculates risk for a single mode of failure. The risk for all modes of failure will be determined to calculate combined risk. Once these are evaluated, fault tree analysis (FTA) can be considered to calculate system risk. Thus, aggregated or unified risk can be minimized for individual components by achieving the target safety level of the system.
If the designer is not satisfied with the system risk overall, the next option is to review the process component parameters in order to identify the most detrimental component responsible for system risk. Once the higher risk/critical components are identified, design parameters can be modified. If the designer is convinced with the revised overall risk, the next task is to move on for detailed design.
It should be noted that the risk-based design methodology is applicable for any pipeline design, both onshore and offshore, with careful consideration given to local environmental conditions, pipeline steel material, loading and operational conditions.
Algorithm Of Risk-Based Design
Using the framework developed in Figure 1, the algorithm of risk-based design is determined as follows:
Step 1: Generate a random variable considering distribution and characteristics values with the required confidence level for both load and resistance.
Step 2: Construct the limit state such as Z = g(R,S) = R - S for load and resistance and calculate the probability of failure using the equations:
[beta] = [[[mu].sub.z]/[[sigma].sub.z]] and [p.sub.f] = 1 - [PHI]([beta])
The mean and standard deviation will be calculated using the first-order, second-moment method (FOSM), the advanced first-order, second-moment method (AFOSM) or the second-order reliability method (SORM) process, whichever is applicable.
Step 3: For each defined failure mode, threat or risk source, apply the Monte Carlo simulation (an alternative approach, where an analytic approach is not applicable) to calculate the probability of failure, based on probabilistic approach.
Step 4: Calculate risk exposure using the equation
Risk = [P.sub.f] x [C.sub.f]
Step 5: If the risk exposure is determined to be higher than acceptable, review the design parameters of the component and recalculate the risk.
Step 6: Up to this point, this has been a component design for a specific mode of failure. Now, calculate all other modes of failure.
Step 7: Integrate the failure modes using FTA with the necessary correlation between the failure modes. If there is no correlation, assume independent failure modes. Find a single probability of failure for a process system.
Correlation of failure modes can be difficult to assess. In cases presenting significant uncertainty, use the worst-case probability as the standard for any group of threats/failure modes as the base case.
Step 8: If the resulting risk level is not acceptable, the designer must assess the most detrimental or high-risk failure modes and consider adjustment of the design parameters of that component. The same procedure is continued across the board until the target safety/risk level is reached. When the target is met, specified design parameters should be selected for final design and construction.
The quantitative RBD approach ensures optimum design without compromising the safety of oil and gas pipelines and risers. The conceptual model also may be considered for similar applications such as probabilistic corrosion rate assessment, reliability-based waste allowance determination and risk-based corrosion management for oil and gas pipelines and risers.
Sikder Mainul Hasan, Jason Everett Hults and Binder Singh, Risk and Integrity Management, Genesis Oil and Gas, Houston
Authors: Sikder Mainul Hasan is an integrity specialist at Genesis, a wholly owned subsidiary of Technip. He is involved in engineering criticality analysis (ECA) or fitness for service (FFS) assessment for offshore structures.
Binder Singh is principal integrity engineer at Genesis Consultancy. His main interests are corrosion, materials performance and integrity management.
Jason Everett Hults is Integrity Management Department manager at Genesis.
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|Author:||Hasan, Sikder Mainul; Hults, Jason Everett; Singh, Binder|
|Publication:||Pipeline & Gas Journal|
|Date:||Nov 1, 2014|
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