Sequence Modeling for Reservoir Characterization
Reservoir characterization involves the estimation petrophysical properties from well logs, core data and seismic data. Estimating such properties is a challenging task due to the non-linearity and heterogeneity of the subsurface. 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 research, we model seismic traces as sequential data and applying state-of-the-art sequence modeling techniques such RNNs and LSTMs for reservoir characterization including, but not limited no, property prediction and facies analysis.