Recent developments combining ensemble smoother and deep generative networks for facies history matching

Ensemble smoothers are among the most successful and efficient techniques currently available for history matching. However, because these methods rely on Gaussian assumptions, their performance is severely degraded when the prior geology is described in terms of complex facies distributions. Inspired by the impressive results obtained by deep generative networks in areas such as image and video generation, we started an investigation focused on the use of autoencoders to construct a continuous parameterization for facies models. In our previous publication, we combined a convolutional variational autoencoder (VAE) with the ensemble smoother with multiple data assimilation (ES-MDA) for history matching production data in models generated with multiple-point geostatistics. Despite the good results reported in our previous publication, a major limitation of the designed parameterization is the fact that it does not allow applying distance-based localization during the ensemble smoother update, which limits its application in large-scale problems. The present work is a continuation of this research project focusing on two aspects: firstly, we benchmark nine different formulations, including VAE, generative adversarial network (GAN), Wasserstein GAN (WGAN), WGAN with gradient penalty, WGAN with spectral normalization, variational auto-encoding GAN, principal component analysis (PCA) with cycle GAN, PCA with transfer style network, and VAE with style loss. These formulations are tested in a synthetic history matching problem with channelized facies. Secondly, we propose two strategies to allow the use of distance-based localization with the deep learning parameterizations.

Automated summary

Ensemble smoothers are effective in history matching, but their performance degrades in complex geology. This study explores the use of autoencoders and proposes two strategies to overcome limitations.