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.
Multiphase flow in fractures has implications to many fields including nuclear waste disposal, CO2 sequestration, geothermal energy, and the oil and gas industry. During multiphase flow, factors that do not play a role in single-phase flow become important. These factors include the interfacial tension between fluids, the fluids viscosity ratio, and the wettability between the solid surfaces and each fluid. Compared to porous media, where the effect of wettability has been extensively researched, the influence of wettability during fracture flow is relatively unstudied. This is partly due to the difficulty in characterizing the wettability of natural rock cores and conducting experiments as well as the difficulty in including wettability into numerical simulations. Therefore, the importance, or lack thereof, of wettability during multiphase fracture flow remains uncertain.