Ensemble Smoother with Fully Convolutional VAE for seismic facies inversion

Seismic facies inversion is an important process in the oil and gas industry to estimate subsurface geological facies or rock types based on seismic data. Recently, the Ensemble Smoother with Multi Data Assimilation (ESMDA) has shown great success in solving complex inverse problems by generating an ensemble of solutions of the model variables for uncertainty quantification. However, there are significant challenges related to the computational cost, Gaussian assumptions, and its application for categorical model variables. Recent developments have proposed to integrate ES-MDA with Variational Autoencoder (VAE) to reparametrize facies into a continuous feature space and reduce computational costs. Despite promising results, it has introduced new limitations concerning network convergence, spatial correlation loss, and manipulation of the latent space. In this paper, a Fully Convolutional Variational Autoencoder (FCVAE) was proposed to outperform the VAE in terms of preserving spatial information, requiring fewer network parameters, presenting higher dimension reduction and yielding better facies estimation. The objective of the application example is to draw sound conclusions based on a 2D synthetic application, where the results of both methods can be compared with reference facies for quality evaluation.