Artificial Neural Network Surrogate Modeling of Oil Reservoir: a Case Study

We develop a data-driven model, introducing recent advances in machine learning to reservoir simulation. We use a conventional reservoir modeling tool to generate training set and a special ensemble of artificial neural networks (ANNs) to build a predictive model. The ANN-based model allows to reproduce the time dependence of fluids and pressure distribution within the computational cells of the reservoir model. We compare the performance of the ANN-based model with conventional reservoir modeling and illustrate that ANN-based model (1) is able to capture all the output parameters of the conventional model with very high accuracy and (2) demonstrate much higher computational performance. We finally elaborate on further options for research and developments within the area of reservoir modeling.

Reconstruction of 3D Porous Media From 2D Slices

We propose a novel deep learning architecture for three-dimensional porous media structure reconstruction from two-dimensional slices. A high-level idea is that we fit a distribution on all possible three-dimensional structures of a specific type based on the given dataset of samples. Then, given partial information (central slices) we recover the three-dimensional structure that is built around such slices. Technically, it is implemented as a deep neural network with encoder, generator and discriminator modules. Numerical experiments show that this method gives a good reconstruction in terms of Minkowski functionals.