Bridging Deep Convolutional Autoencoders and Ensemble Smoothers for Improved Estimation of Channelized Reservoirs

One of the main problems associated with applying data assimilation methods for facies models is the lack of geological plausibility in updates. This issue is even more acute for channelized reservoirs, knowing that, without a reliable parameterization, the geometrical structure of the channels can hardly be reproduced in the updated step of any data assimilation method. This paper presents a new methodology for estimation and uncertainty quantification of facies fields in channelized reservoirs, bridging a deep convolutional autoencoder with an ensemble-based method. The proposed methodology is suitable for any geological simulation model and does not use the resampling from the training image when using a multipoint geostatistical simulation model. Besides the channel estimation, the proposed methodology preserves the geological plausibility in the updated step of the history-matching method. The new methodology employs, inside the parameterization, a deep convolutional autoencoder to reconstruct the channel geometry. The convolutional autoencoder is used for image reconstruction purposes. The input of the training set of the autoencoder consists of images (facies fields) generated with a parameterization of the facies fields and perturbed with a Gaussian noise having spatial correlation. This procedure ensures the consistency of the method in the sense that the input fields have a similar structure with the facies fields obtained after the history matching. The methodology is tested for channelized reservoirs, with different levels of complexity, and also by comparison with previous methods that use or not resampling from the training image. The results show an improvement in the geological plausibility, estimation, and uncertainty quantification of the channel distributions while achieving a good data match.

Automated summary

This paper presents a new methodology that combines deep convolutional autoencoder with ensemble-based method for estimating and quantifying uncertainty of facies fields in channelized reservoirs. The proposed methodology reconstructs the geometry of channels inside the parameterization, preserving geological plausibility and achieving good results in experiments.