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Rock physics models (RPMs) are used to estimate the elastic properties (e.g.velocity, moduli) from the rock properties (e.g. porosity, lithology, fluidsaturation). However, the rock properties drastically vary for differentgeological conditions, and it is not easy to find a model that is applicableunder all scenarios. There exist several empirical velocity-porosity transformsas well as first-principle-based models, however, each of these has its ownlimitations. It is not very straight-forward to choose the correct RPM, andtemplates exist, which are overlapped with the log data to decide on thecorrect model. In this work, we use deep machine learning and explore theconcept of designing a supermodel that can be used for several differentlithological conditions without any parameter tuning. In this paper, this testis restricted to only empirical velocity-porosity transforms, however, thefuture goal is to design a rock physics supermodel that can be used on avariety of rock properties. The goal of this paper to is to combine theadvantages of several existing empirical velocity-porosity transforms under asingle framework, and design a velocity-porosity supermodel (VPS) usingartificial neural networks (ANN) based deep learning. Two test cases are usedand based on the results presented in this paper, it is clear that deep neuralnetworks can be a potential tool to develop a supermodel for lithologicalmodeling and characterization.

Velocity-Porosity Supermodel: A Deep Neural Networks based concept