This paper presents two representative models to analyze the flow dynamic of multi-scale porous medium in hydraulic fractured horizontal shale gas wells. In this work, considering the characteristic mechanisms (multi-scale porous space, desorption and diffusion), flow equations in shale are established. After that, two representative models (discrete fracture model and dual-porosity model) are tailored to our issues. Solved by the control-volume finite element method (CVFEM), influences of fracture network system on productivity in shale reservoirs are analyzed in detail. Based on the analysis, the effects can be summarized as follow: at the beginning of production, high conductivity fracture network means more free gas could be produced; at the later part of production, high conductive fracture network can form a large low pressure region, which can not only stimulate the desorption of adsorbed gas, but also reduce the flow resistance to the well. Finally, the sensitivities of characteristic parameters in shale are discussed.

Combining the advantages of multicontinuum and multifracture representations provides an easy-to-use tool to adequately capture the characteristic of the multiscaled fracture system in shale gas reservoir. A hybrid model is established on the basis of simplified conceptual productivity assumption, where the matrix volume is divided into two sub-domains (triple-porosity model and dual-depletion flowing model) and the fracture volume is represented by discrete finite conductivity fracture. In addition, the mechanisms of instant desorption, viscous flow and dual-depletion in matrix are taken into account. The rate transient responses are then obtained by use of semi-analytical approach. Based on the model, type curves are plotted and verified by comparing with alternative reliable methods. Different flow regimes in shale gas reservoirs can be identified and detected. The Generalized Likelihood Uncertainty Estimation methodology, based on probabilistic aggregation theory, is employed to integrating those two productivity models together such that the production can be predicted more accurately. A field example is applied to validate the applicability of this new model. Finally, it is concluded that the proposed model can predict the rate and cumulative rate more easily and practically.