  Rock physics models (RPMs) are used to estimate the elastic properties (e.g.
velocity, moduli) from the rock properties (e.g. porosity, lithology, fluid
saturation). However, the rock properties drastically vary for different
geological conditions, and it is not easy to find a model that is applicable
under all scenarios. There exist several empirical velocity-porosity transforms
as well as first-principle-based models, however, each of these has its own
limitations. It is not very straight-forward to choose the correct RPM, and
templates exist, which are overlapped with the log data to decide on the
correct model. In this work, we use deep machine learning and explore the
concept of designing a supermodel that can be used for several different
lithological conditions without any parameter tuning. In this paper, this test
is restricted to only empirical velocity-porosity transforms, however, the
future goal is to design a rock physics supermodel that can be used on a
variety of rock properties. The goal of this paper to is to combine the
advantages of several existing empirical velocity-porosity transforms under a
single framework, and design a velocity-porosity supermodel (VPS) using
artificial neural networks (ANN) based deep learning. Two test cases are used
and based on the results presented in this paper, it is clear that deep neural
networks can be a potential tool to develop a supermodel for lithological
modeling and characterization.
