Prediction of permeability of porous media using optimized convolutional neural networks

Permeability is an important parameter to describe the behavior of a fluid flow in porous media. To perform realistic flow simulations, it is essential that the fine scale models include permeability variability. However, these models cannot be used directly in simulations because require high computational cost, which motivates the application of upscaling approaches. In this context, machine learning techniques can be used as an alternative to perform the upscaling of porous media properties with lower computational cost than traditional upscaling methods. Hence, in this work, an upscaling methodology is proposed to compute the equivalent permeability on the large grid through convolutional neural networks (CNN). This method achieves suitable precision, with less computational demand, when evaluated on 2D and 3D models, if compared with the local upscaling approach. We also present a genetic algorithm (GA) to automatically determine the optimal configuration of CNNs for the target problems. The GA procedure is applied to yield the optimal CNN architecture for upscaling of the permeability fields with outstanding results when compared with counterpart techniques.