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Probabilistic Framework for Seismic Risk Analysis of Industrial Plants of the Oil Infrastructure

Kamali Shakib, Mohammad Javad | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55986 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Mahsouli, Mojtaba
  7. Abstract:
  8. This research proposes a probabilistic framework for seismic risk analysis of industrial plants of oil infrastructure using reliability methods. The proposed approach integrates multiple probabilistic models and system reliability to quantify the seismic risk of the process plants. A chain of probabilistic seismic hazard and risk models is utilized for the risk analysis. The Monte Carlo sampling method is used to propagate the significant uncertainty in the hazard event, damage to components and its consequences, and the potential losses incurred by process plant. In each sample of the analysis, hazard models simulate the occurrence, magnitude, and rupture area and location of earthquake events and the ensuing ground shaking intensity at the location of components. Risk models start with the seismic damage model that receives the hazard intensity as input. Using fragility models, this model randomly simulates the damage to components based on the intensity measure. Subsequently, the serviceability status of each equipment is predicted based on its damage and its dependency on the power station. Repair cost and repair time models respectively predict the component repair costs and time based on the incurred damage. The network restoration time model calculates the required time for the network to return to its normal condition based on the component repair time. To model the network, equipment in three areas of the process, utilities, and storage are modeled as nodes, and pipelines are modeled as edges. The position of each equipment in the process and its connection type with other equipment is determined in the process flow diagram. The network model receives the status of equipment serviceability and pipeline functionality as inputs. Then, by determining cut sets of the system, the failure status of terminal components, i.e. the end equipment or pipeline of each production line, is determined immediately after the earthquake event. The network model is developed at the connectivity-based refinement level where the failure or nonfailure status of the process is determined by applying Boolean logic on components failure status. In the end, a model predicts the production disruption cost. This model receives the failure status of terminal components and the network restoration time model output as input and predicts the production disruption cost of network flow lines as output. Finally, the exceedance probability curve of the mentioned losses is obtained by repeating the sampling process, which represents the complete seismic risk in the industrial unit. The proposed approach and probabilistic models have been implemented in Rtx, which is a computer program for reliability, risk and resilience analysis with a comprehensive library of probabilistic models for various infrastructure systems. The proposed approach is showcased by an application to a virtual nitric acid chemical plant. This plant consists of two physical production lines, 73 components including equipment and pipeline, and one power station. The plant is considered to be located at the south of Tehran, which is affected by 28 simple fault sources and 13 area sources within a 150-kilometer radius. Two methods of load combination and scenario sampling are used to compute the 50-year maximum probability distribution of total losses including repair cost and production disruption cost. The effect of equipment and pipeline deterioration and the impact of discounting on losses are considered in separate scenarios and compared in cases without deterioration and discounting. In addition, scenario-based seismic risk of the plant is evaluated for the occurrence of earthquakes with maximum magnitudes and return periods of 475 and 2475 years. Other results of this analysis include the fragility curve of the entire industrial unit, which provides the failure probability of system conditioned on earthquake intensity, and the ranking of components of the industrial unit based on their vulnerability. According to the results of the risk analysis, losses resulting from production downtime account for the largest portion of total losses. This highlights the importance of modeling the system and the consequences of service disruption. In all scenarios, the storage tanks and power station were found to be the most vulnerable. Furthermore, the occurrence of an earthquake with return periods of 475 and 2475 years disrupts the industrial the process network of the industrial unit for 714 and 1893 days, respectively
  9. Keywords:
  10. Fragility Curve ; Probability Model ; Probabilistic Seismic Structures Vulnerability ; Risk Analysis ; Seismic Hazard Analysis ; System Reliability ; Monte Carlo Sampling ; Oil Infrastructure ; Industrial Units

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