Reconstructing the 3D digital core with a fully convolutional neural network*
Li Qiong1, Chen Zheng 1, He Jian-Jun 2, Hao Si-Yu 3, Wang Rui 1, Yang Hao-Tao 1, and Sun Hua-Jun
1. College of Geophysics, Chengdu University of Technology, Chengdu 610059, China.
2. College of Information Science & Technology (College of Cybersecurity, Oxford Brookes University), ChengduUniversity of Technology, Chengdu 610059, China.
3. China Mobile Communications Group Sichuan Co., Ltd. Chengdu Branch, Chengdu 610041, China.
Abstract In this paper, the complete process of constructing 3D digital core by full convolutional neural network is described carefully. A large number of sandstone computed tomography (CT) images are used as training input for a fully convolutional neural network model. This model is used to reconstruct the three-dimensional (3D) digital core of Berea sandstone based on a small number of CT images. The Hamming distance together with the Minkowski functions for porosity, average volume specific surface area, average curvature, and connectivity of both the real core and the digital reconstruction are used to evaluate the accuracy of the proposed method. The results show that the reconstruction achieved relative errors of 6.26%, 1.40%, 6.06%, and 4.91% for the four Minkowski functions and a Hamming distance of 0.04479. This demonstrates that the proposed method can not only reconstruct the physical properties of real sandstone but can also restore the real characteristics of pore distribution in sandstone, is the ability to which is a new way to characterize the internal microstructure of rocks.
This work was sponsored by the National Natural Science Foundation of China (No. 41274129), Chuan Qing Drilling Engineering Company's Scientific Research Project: Seismic detection technology and application of complex carbonate reservoir in Sulige Majiagou Formation and the 2018 Central Supporting Local Co-construction Fund (No. 80000-18Z0140504), the Construction and Development of Universities in 2019 - Joint Support for Geophysics (Double First-Class center, 80000-19Z0204).
Corresponding Authors: Li Qiong (Email: liqiong@cdut.cn)
E-mail: liqiong@cdut.cn
About author: Li Qiong received her Ph.D. in Earth Exploration and Information Technology (2007) from Chengdu University of Technology, China. She is currently a professor at the College of Geophysics of Chengdu University of Technology, China. Her research interests are on seismic rock physics and geophysics of complex reservoirs.
Cite this article:
. Reconstructing the 3D digital core with a fully convolutional neural network*[J]. APPLIED GEOPHYSICS, 2020, 17(3): 401-410.
2020, Vol. 17 
