RockGPT: reconstructing three-dimensional digital rocks from single two-dimensional slice with deep learning

Zheng，Qiang1; Zhang，Dongxiao2

2022

10.1007/s10596-022-10144-8

COMPUTATIONAL GEOSCIENCES   影响因子和分区

1420-0597

1573-1499

Random reconstruction of three-dimensional (3D) digital rocks from two-dimensional (2D) slices is crucial for elucidating the microstructure of rocks and its effects on pore-scale flow in terms of numerical modeling, since massive samples are usually required to handle intrinsic uncertainties. Despite remarkable advances achieved by traditional process-based methods, statistical approaches and recently famous deep learning-based models, few works have focused on producing several kinds of rocks with one trained model and allowing the reconstructed samples to approximately satisfy certain given properties, such as porosity. To fill this gap, we propose a new framework with deep learning, named RockGPT, which is composed of VQ-VAE and conditional GPT, to synthesize 3D samples based on a single 2D slice from the perspective of video generation. The VQ-VAE is utilized to compress high-dimensional input video, i.e., the sequence of continuous rock slices, to discrete latent codes and reconstruct them. In order to obtain diverse reconstructions, the discrete latent codes are modeled using conditional GPT in an autoregressive manner, while incorporating conditional information from a given slice, rock type, and porosity. We conduct two experiments on five kinds of rocks, and the results demonstrate that RockGPT can produce different kinds of rocks with a single model, and the porosities of reconstructed samples can distribute around specified targets with a narrow range. In a broader sense, through leveraging the proposed conditioning scheme, RockGPT constitutes an effective way to build a general model to produce multiple kinds of rocks simultaneously that also satisfy user-defined properties.

Conditional generation Digital rock RockGPT Three-dimensional reconstruction

SCI

英语

通讯

Shenzhen Key Laboratory of Natural Gas Hydrates[ZDSYS20200421111201738] ; China Postdoctoral Science Foundation[2020 M682830]

Computer Science ; Geology

Computer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary

WOS:000781002600001

SPRINGER

COMPUTER SCIENCE

2-s2.0-85128074239

Scopus

1.Department of Mathematics and Theories,Peng Cheng Laboratory,Shenzhen,518000,China
2.Shenzhen Key Laboratory of Natural Gas Hydrates,Southern University of Science and Technology,Shenzhen,518055,China

Zheng，Qiang,Zhang，Dongxiao. RockGPT: reconstructing three-dimensional digital rocks from single two-dimensional slice with deep learning[J]. COMPUTATIONAL GEOSCIENCES,2022.

Zheng，Qiang,&Zhang，Dongxiao.(2022).RockGPT: reconstructing three-dimensional digital rocks from single two-dimensional slice with deep learning.COMPUTATIONAL GEOSCIENCES.

Zheng，Qiang,et al."RockGPT: reconstructing three-dimensional digital rocks from single two-dimensional slice with deep learning".COMPUTATIONAL GEOSCIENCES (2022).