Stochastic modeling of coal fracture network by direct use of micro-computed tomography images

Characterization of coalbed methane reservoirs is a challenging task because of complex petrophysical properties of coal. Coal cleat system has a key role in permeability of gas through coalbed. Previous computational methods for characterization and modeling in coal formations do not account for the actual complexities in cleat systems as they commonly rely on simple statistical properties for describing the fractures. In this study, unlike the previous methods that try to extract some of the spatial statistical properties, the 2D/3D micro computed tomography images are used directly without any simplifications and assumptions. The generated models are compared to discrete fracture networks as one of the widely-used method for the modeling of such complex systems of coal cleats. Results show that the utilized algorithm produces visually satisfactory realizations of both coal matrix and cleat system. To quantify such similarities, autocorrelation functions, connectivity (with two distinct indices), average fracture length and orientation are computed. Moreover, permeabilities and porosities of the reconstructed samples are calculated and compared with the original sample. It is demonstrated that the proposed reconstruction method reproduces samples with similar statistical and petrophysical properties, but with different patterns of both coal porous region and fracture system. Finally, the proposed method and the DFN realizations are also compared extensively. The results of this study can be used for characterization of coal samples with any degree of complexity and heterogeneity by producing several realistic stochastic models. Consequently, petrophysical properties and their corresponding uncertainties can be evaluated more accurately.