Seismic attribute analysis with a combination of convolutional autoencoder and random forest in a turbidite reservoir

Due to the complex depositional environment of a turbidite reservoir in the Niuzhuang Delta, China, the traditional seismic facies classification is a challenge to perform accurately and continuously. Due to the thin turbidite layers in the reservoir, machine-learning-based prediction of sandstone thickness is challenging. Inspired by the autoencoder, we develop an open-source deep-learning workflow that combines unsupervised and supervised learning with jointed latent eigenvalues of the convolutional autoencoder (CAE) and traditional seismic attributes for seismic facies classification and sandstone thickness prediction constrained by the facies distribution. First, we extract lower-dimensional latent eigenvalues as a category of novel seismic attributes from the seismic data using a CAE. To accurately and effectively extract lower-dimensional latent eigenvalues, we develop a hybrid loss function based on the mean-squared error loss and the smooth L1 loss in this CAE. Then, we use principal component (PC) analysis to extract the first four PCs of these seismic lower-dimensional latent eigenvalues. Using unsupervised K-means, we cluster the first four PCs to form seismic facies. Finally, we take the first four PCs with the traditional seismic attributes as input and the sandstone thickness as labels for the random forest to predict the sandstone thickness distribution. The results of seismic facies and sandstone thickness distribution confirm the potential and advantages of our workflow, which can speed up the identification of seismic facies with smoother boundaries, improve the prediction accuracy by 16% over than that of traditional seismic attributes, and provide more depositional insight for a turbidite reservoir of the Shahejie Formation in the Niuzhuang Delta, China.