Failure risk assessment of interdependent infrastructures against earthquake,a Petri net approach: case study—power and water distribution networks

Metropolitan areas consist of complicated systems of interconnected infrastructures that are highly interdependent. Disruption of one infrastructure may induce disruption in other interconnected ones. The results from analysis of one infrastructure as an independent system are not realistic without considering the behavior of other interconnected infrastructures. Consequently, the study of the interdependencies among critical infrastructures is important for addressing the cascading effects of a failed infrastructure on the entire network to properly model its performance and help the disaster management team in decision making. In this study, the extended Petri net and Markov chain have been used to demonstrate the power and water infrastructure interdependency with a case study of one of the municipal districts of metropolitan Tehran, the capital of Iran. In this research, three cases have been assessed quantitatively: (1) the intra-dependency effects of different components in each network, (2) the interdependency effects between the considered critical infrastructures and (3) the behavior of the water network considering intra- and interdependency, when the power network fails. The analyses show that considering the mentioned interdependencies has a major influence on their performance simulations and assessment of their exact vulnerability. It is concluded that the failure probability of the water network that is dependent on the failed power network is 1.66 of the independent water network in the studied region. Eventually, the results of the research could be used in design, restoration and disaster management planning for safety assessment of critical infrastructures.     

Optimizing compaction calculation for improved probabilistic 2D mapping

Integration of all available data in reservoir characterization is critically important. 2D mapping is a reliable and robust technique that allows integration of multiple secondary data, including geological and geophysical surfaces and maps, to generate realistic summaries of reservoir quality at each location in an area of interest with an associated measure of uncertainty. This is achieved in 2D mapping with a more straightforward implementation, requiring significantly less time and fewer resources than three-dimensional modelling. In this paper, we propose an approach for the empirical calculation and optimization of differential compaction maps by leveraging existing well control for the use in 2D mapping. Success of the proposal is demonstrated through tests of accuracy, precision and fairness of the local uncertainty distributions for 100 new stratigraphical wells drilled in the Christina Lake and Kirby East area.