Multiphase flow through fractures is common in many fields, yet our understanding of the process remains limited. In general, this is because some factors which separate multiphase flow from single-phase flow (interfacial tension, wettability, residual saturation) are difficult to characterize and control in a laboratory setting, and are also challenging to implement in traditional numerical simulators. Here, we present a series of lattice Boltzmann simulations of CO2 displacing brine in rough fractures with heterogeneous wettability. This extended abstract focuses on the application of this technique to predict irreducible brine saturation within the fractures. We show that this irreducible brine saturation may be greater than 25%, which could have significant impacts on production estimates from unconventional reservoirs and is typically not accounted for in reservoir simulators. However, performing these simulations at the field scale is not possible due to their computational expense. Therefore, we present a machine learning technique based on deep neural networks to predict the fluid distribution within these fractures at steady state trained upon on the lattice Boltzmann simulations. To our knowledge, this is the first example of machine learning being used to predict the distribution of fluid within a subsurface media. Here we show that a trained network is able to accurately predict the fluid residual saturation and distribution based solely on the dry fracture characteristics. This proves that machine learning holds promise for upscaling these simulations to a relevant scale for application to the oil and gas industry.