Enhancing the Parameterization of Reservoir Properties for Data Assimilation Using Deep VAE-GAN

TL;DR

This paper introduces a novel deep learning model called VAE-GAN, which combines the strengths of VAEs and GANs to improve reservoir property parameterization for data assimilation, achieving realistic models and accurate history matching.

Contribution

The work innovatively integrates VAE and GAN into a single model for reservoir parameterization, enhancing data assimilation accuracy and geological realism.

Findings

VAE-GAN produces high-quality reservoir models.

VAE-GAN achieves good history matching.

The method outperforms individual VAE or GAN approaches.

Abstract

Currently, the methods called Iterative Ensemble Smoothers, especially the method called Ensemble Smoother with Multiple Data Assimilation (ESMDA) can be considered state-of-the-art for history matching in petroleum reservoir simulation. However, this approach has two important limitations: the use of an ensemble with finite size to represent the distributions and the Gaussian assumption in parameter and data uncertainties. This latter is particularly important because many reservoir properties have non-Gaussian distributions. Parameterization involves mapping non-Gaussian parameters to a Gaussian field before the update and then mapping them back to the original domain to forward the ensemble through the reservoir simulator. A promising approach to perform parameterization is through deep learning models. Recent studies have shown that Generative Adversarial Networks (GAN) performed poorly concerning data assimilation, but generated more geologically plausible realizations of the reservoir, while the Variational Autoencoder (VAE) performed better than the GAN in data assimilation, but generated less geologically realistic models. This work is innovative in combining the strengths of both to implement a deep learning model called Variational Autoencoder Generative Adversarial Network (VAE-GAN) integrated with ESMDA. The methodology was applied in two case studies, one case being categorical and the other with continuous values of permeability. Our findings demonstrate that by applying the VAE-GAN model we can obtain high quality reservoir descriptions (just like GANs) and a good history matching on the production curves (just like VAEs) simultaneously.