The fracture network plays a critical role in controlling flow pathways in fractured rock. Thus, transmissibility study of fracture networks via different flow modelling methods is of importance. Compared with direct flow simulation, the pipe network model is an effective means of modelling fluid flow in fracture network due to its computational efficiency. However, the characterisation of the fracture network topology as well as the equivalent conductivity of the pipes are still challenging to credibly predict the permeability. Also, pipe network models are commonly constructed based on stochastic Discrete Fracture Network (DFN) models with more uncertainties, which also requires a number of stochastic DFN realisations to be created. In this paper, we develop a novel Pipe Network Modelling (PNM) framework for fractured media, where the PNM is constructed based on deterministic DFN models that are directly derived from micro-CT images. By comparing permeability values obtained from PNMs and results from micro-CT images and voxelised DFNs, we conclude that PNM modelling can effectively estimate the permeability of original fracture networks, while requiring significantly less computational cost. In addition to the advantage of computational efficiency, PNM is more preferable for the challenging multi-phase flow simulation.