Reconstructing the 3D digital core with a fully convolutional neural network  

作　　者：Li Qiong Chen Zheng He Jian-Jun Hao Si-Yu Wang Rui Yang Hao-Tao Sun Hua-Jun 

机构地区：[1]College of Geophysics,Chengdu University of Technology,Chengdu 610059,China. [2]College of Information Science&Technology(College of Cybersecurity,Oxford Brookes University),Chengdu University of Technology,Chengdu 610059,China. [3]China Mobile Communications Group Sichuan Co.,Ltd.Chengdu Branch,Chengdu 610041,China.

出　　处：《Applied Geophysics》2020年第3期401-410,共10页应用地球物理（英文版）

基　　金：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)。

摘　　要：In this paper, the complete process of constructing 3D digital core by fullconvolutional neural network is described carefully. A large number of sandstone computedtomography (CT) images are used as training input for a fully convolutional neural networkmodel. This model is used to reconstruct the three-dimensional (3D) digital core of Bereasandstone based on a small number of CT images. The Hamming distance together with theMinkowski functions for porosity, average volume specifi c surface area, average curvature,and connectivity of both the real core and the digital reconstruction are used to evaluate theaccuracy of the proposed method. The results show that the reconstruction achieved relativeerrors of 6.26%, 1.40%, 6.06%, and 4.91% for the four Minkowski functions and a Hammingdistance of 0.04479. This demonstrates that the proposed method can not only reconstructthe physical properties of real sandstone but can also restore the real characteristics of poredistribution in sandstone, is the ability to which is a new way to characterize the internalmicrostructure of rocks.

关 键 词：Fully convolutional neural network 3D digital core numerical simulation training set 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] P624[自动化与计算机技术—控制科学与工程]