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Reservoir characterization involves the estimation petrophysical propertiesfrom well-log data and seismic data. Estimating such properties is achallenging task due to the non-linearity and heterogeneity of the subsurface.Various attempts have been made to estimate petrophysical properties usingmachine learning techniques such as feed-forward neural networks and supportvector regression (SVR). Recent advances in machine learning have shownpromising results for recurrent neural networks (RNN) in modeling complexsequential data such as videos and speech signals. In this work, we propose analgorithm for property estimation from seismic data using recurrent neuralnetworks. An applications of the proposed workflow to estimate density andp-wave impedance using seismic data shows promising results compared tofeed-forward neural networks.

Petrophysical Property Estimation from Seismic Data Using Recurrent Neural Networks