Development and testing of two-phase relative permeability predictors using artificial neural networks

In this paper, we report liquid/liquid and liquid/gas two-phase relative permeability predictors that are developed using artificial neural networks (ANNs). In the development stage, some of the relative permeability data from literature are used during the training stage while some other sets are preserved to test the prediction capabilities of the models. Various rock and fluid properties, including endpoint saturations, porosity, permeability, viscosity and interfacial tension, and some functional links (mathematical groups coupling various rock and fluid properties) constitute the input parameters of the models. The models are found to successfully predict the field and experimental relative permeability data.