Journal
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
Volume 177, Issue -, Pages 941-958Publisher
ELSEVIER
DOI: 10.1016/j.petrol.2019.02.037
Keywords
Reservoir history matching; Geological facies; Deep learning; Deep belief network; Ensemble smoother
Categories
Funding
- Petrobras, Brazilian OilGas company
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The current research trend in history matching is to consider more realistic reservoir models with complex geology. Hence, it is important to be able to update facies models during the solution. Although Kalman filter-based methods have been applied with success in several real-life history-matching problems, their performance is severely degraded when the prior geology is described in terms of complex facies distributions, since these methods rely on Gaussian assumptions. This paper investigates a novel parameterization based on deep learning techniques for proper history matching of facies models with methods based on the Kalman filter. The proposed method consists on a parameterization of geological facies by means of a deep generative model, with a deep belief network used as an autoencoder. To perform the history matching, we use the ensemble smoother with multiple data assimilation (ES-MDA) that iteratively updates the model to account the observed production data. The proposed method is compared to the standard ES-MDA and ES-MDA combined with optimization-based principal component analysis (OPCA). The results showed clear improvements over the standard ES-MDA in terms of preserving channelized features in the realizations and a performance comparable to the parameterization with OPCA.
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