Efficient deep-learning-based history matching for fluvial channel reservoirs

In history matching, the calibration of a prior reservoir model is computationally expensive because many forward reservoir simulation runs are required. Multiple posterior (or calibrated) reservoir models need to be sampled to consider high reservoir uncertainty, which increases the computational cost significantly. In this study, we propose a novel deep-learning-based history matching method that efficiently samples posterior reservoir models for fluvial channel reservoirs. Three convolution-based neural networks (NNs) are used in the proposed method to sample posterior models quickly without conventional calibration processes: convolutional autoencoder (CAE), convolutional neural network (CNN), and convolutional denoising autoencoder (CDAE). First, low-dimensional latent features are extracted from prior models using CAE because the dimensionality of static data is too high to find the relation between the prior models and corresponding simulated dynamic (production) data. Next, CNN is used to find the relation between the latent features of the prior models and the corresponding production data, which are the output and input data of CNN, respectively. The CNN output is refined using CDAE to improve the geological connectivity of the posterior models. The performance of the proposed method is compared with non-convolution-based methods that combine fully-connected NN structures (multi-layer perceptron (MLP)) and dimension-reduction techniques (principal component analysis (PCA) and stacked autoencoder (SAE)) in the benchmark egg model. The proposed method outperforms the other methods (MLP-PCA and MLP-SAE) in terms of geological constraints for fluvial channels and the computational cost of sampling posterior models.