2019/01/29 by Denis Volkhonskiy, Volkhonskiy, Denis, Ekaterina Muravleva +12
Computer Science · Earth and Planetary Sciences · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #Enhanced Oil Recovery Techniques #FOS: Computer and information sciences #Hydrocarbon exploration and reservoir analysis #Seismic Imaging and Inversion Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1901.10233
openalex publication_date 2019/01/29 · arxiv created 2021/08/06 · arxiv updated 2021/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In many branches of earth sciences, the problem of rock study on the micro-level arises. However, a significant number of representative samples is not always feasible. Thus the problem of the generation of samples with similar properties becomes actual. In this paper, we propose a novel deep learning architecture for three-dimensional porous media reconstruction from two-dimensional slices. 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 around such slices as the most probable one according to that constructed distribution. Technically, we implement this in the form of a deep neural network with encoder, generator and discriminator modules. Numerical experiments show that this method provides a good reconstruction in terms of Minkowski functionals.