Comparative Study Of Deep Feed Forward Neural Network Application For Seismic Reservoir Characterization

Abstract

Machine learning has been gaining momentum thanks to a new powerful technique called deep learning (Bengio, 2016). These improvements are due to increasing the depth of neural networks to more than one hidden layer. This study uses a Deep Feed-forward Neural Network (DFNN) to predict reservoir properties from seismic attributes similar to Hampson et al. (2001). These are shale, porosity and water saturation volumes, ultimately allowing the estimation of the net pay volume. We compare the results of the DFNN to other forms of machining learning such as multi-linear regression (MLR), Probabilistic Neural Network (PNN).