In oil/gas and chemical industries, dynamics is one of the most essential characteristics
of any process. Time-dependent response is involved in most steps of both
the physical/engineering processes and the equipment performance. The conventional
Quantitative Risk Assessment (QRA) is unable to address the time dependent effect
in such dynamic processes. In this dissertation, a methodology of Dynamic Operational
Risk Assessment (DORA) is developed for operational risk analysis in oil/gas
and chemical industries. Given the assumption that the component performance state
determines the value of parameters in process dynamics equations, the DORA probabilistic
modeling integrates stochastic modeling and process dynamics modeling to
evaluate operational risk. The stochastic system-state trajectory is modeled based on
the abnormal behavior or failure of the components. For each of the possible system-state
trajectories, a process dynamics evaluation is carried out to check whether
process variables, e.g., level, flow rate, temperature, pressure, or chemical concentration,
remain in their desirable regions. Monte Carlo simulations are performed to
calculate the probability of process variable exceeding the safety boundaries. Component
testing/inspection intervals and repair time are critical parameters to define the
system-state configuration; and play an important role for evaluating the probability
of operational failure. Sensitivity analysis is suggested to assist selecting the DORA probabilistic modeling inputs. In this study, probabilistic approach to characterize
uncertainty associated with QRA is proposed to analyze data and experiment results
in order to enhance the understanding of uncertainty and improve the accuracy of
the risk estimation. Different scenarios on an oil/gas separation system were used
to demonstrate the application of DORA method, and approaches are proposed for
sensitivity and uncertainty analysis. Case study on a knockout drum in the distillation
unit of a refinery process shows that the epistemic uncertainty associated with
the risk estimation is reduced through Bayesian updating of the generic reliability
information using plant specific real time testing or reliability data. Case study on
an oil/gas separator component inspection interval optimization illustrates the cost benefit
analysis in DORA framework and how DORA probabilistic modeling can be
used as a tool for decision making. DORA not only provides a framework to evaluate
the dynamic operational risk in oil/gas and chemical industries, but also guides
the process design and optimization of the critical parameters such as component
inspection intervals.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-05-7755 |
| Date | 2010 May 1900 |
| Creators | Yang, Xiaole |
| Contributors | Mannan, M. Sam |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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