Predicting the fluid flow behaviour of systems containing fractures or other flow conduits (which are typically sub-seismic in scale) is an important element of flow modelling in petroleum exploration and production. Since the fluid flow can be strongly influenced by multiple flow control factors, including the connectivity of the fracture network, the spatial distribution of fractures (e.g. fracture intensity), and the contrast of flow properties between fractures and the matrix, the fluid flow behaviour of a fractured system, with respect to flow control factors, may show multiple distinctive modes. Clearly the existence of different modes indicates a need for a multi-modal approach to incorporating effective flow properties in the coarser-scale flow simulation. However, the intrinsic non-linearity of such relationships and the high dimensionality of the factors make it difficult to identify such relationships, let alone to distinguish the distinctive modes among them. <br><br>In this work, a non-linear analysis, based on a machine learning method - Support Vector Machine (SVM), was considered for identifying the relationships. It was applied to two simple fractured systems in 2D, each of which was characterised by a set of distributional parameters and a stochastic fracture modelling procedure. For each system, equally-plausible fracture models were generated, and the single-phase steady-state fluid flow was simulated for each model. The relationships between the simulated fluid flow, in a form of upscaled permeability, and a number of flow control factors, were then analysed. The results showed the existence of complex and multi-modal relationships between the upscaled permeability and the control factors in each system, with distinctive features between the systems. The implications of not employing a multi-modal approach in a coarser-scale simulation are obvious: the upscaled flow properties can be significantly mis-estimated.<br><br>


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