The overall aim of the ProGasNet (Probabilistic Gas Network Simulator) project is to develop European gas transmission network probabilistic model capable to analyse reliability and risk aspects of the network under normal load or crisis situations. The gas transmission network reliability modeling is a challenging task especially considering time dependent storage supply. The gas storage availability and use can be essential under crisis scenarios. The paper will present extension of the existing probabilistic Monte-Carlo based gas network model capable to model 1-day snapshot network performance to longer periods of up to 45 days. This extension is a challenge both computationally and algorithmically as dynamic effects start to play an important role in the model. The study focus on time-dependent storage discharge modelling being affected by random equipment failures. The probabilistic model runs Monte Carlo simulations to generate random network failures and then employs graph theory maximum flow algorithm to estimate available gas to all demand nodes at each time step. The model framework enables to study different crisis scenarios under different supply and demand patterns. The study results are shown on a testing frame of the European gas network, however geographical infor-mation is not explicitly displayed. The effects of storage supply are explored under potential crisis scenarios and recommendations are provided for optimised network management and response to crisis situations. ; JRC.F.3-Energy Security, Systems and Market