  Reservoir characterization involves the estimation petrophysical properties
from well-log data and seismic data. Estimating such properties is a
challenging task due to the non-linearity and heterogeneity of the subsurface.
Various attempts have been made to estimate petrophysical properties using
machine learning techniques such as feed-forward neural networks and support
vector regression (SVR). Recent advances in machine learning have shown
promising results for recurrent neural networks (RNN) in modeling complex
sequential data such as videos and speech signals. In this work, we propose an
algorithm for property estimation from seismic data using recurrent neural
networks. An applications of the proposed workflow to estimate density and
p-wave impedance using seismic data shows promising results compared to
feed-forward neural networks.
