基于二维训练图像的多点统计重建三维多孔介质

Yuqi Wu, Chengyan Lin, L. Ren, Weichao Tian, Yang Wang, Yimin Zhang
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引用次数: 2

摘要

多孔介质的宏观输运性质主要取决于其孔隙空间的几何形状和拓扑结构。预测这些输运性质的前提是建立一个精确的三维孔隙空间。迄今为止,模拟多孔介质的方法主要分为两大类:用某些设备直接测量和随机统计方法。现有的设备,如x射线计算机断层扫描和激光扫描共聚焦显微镜,可以直接测量孔隙结构,但设备的不可获得性和测量的高成本使它们无法广泛应用。许多随机统计方法,如截尾高斯随机场和模拟退火方法,都是基于一些二维薄片,利用低阶统计函数重建三维多孔介质。然而,这些函数并不能再现孔隙结构的长期连通性。因此,本文将提出一种利用多点统计重建三维孔隙空间的随机技术,以解决上述问题。单正态方程模拟算法(SNESIM)是多点统计中最常用的离散变量模拟方法之一,是再现孔隙空间长程特征的主要工具。为了验证该方法,Berea砂岩被用作样本。在模拟过程中,采用二维薄片作为提供孔隙结构模式的训练图像,并从中提取部分像素作为调节数据。利用SNESIM算法作为仿真引擎对模型进行重构。为了验证这些重建模型的准确性,将重建模型的孔隙几何形状、拓扑结构和输运性质与x射线计算机断层扫描获得的真实模型进行了比较。对比结果表明,重建模型在两点相关函数、孔隙空间特征、单、两相渗流渗透率等方面与x射线计算机断层扫描得到的真实模型吻合较好,验证了该方法可以再现孔隙空间的远程连通性。与其他随机方法相比,提出了一种在只有部分二维薄片的情况下更精确地重建三维多孔介质的随机方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reconstruction of 3D Porous Media Using Multiple-Point Statistics Based on a 2D Training Image
Macroscopic transport properties of porous media essentially rely on the geometry and topology of their pore space. The premise of predicting these transport properties is to construct an accurate 3D pore space. To date the methods of modeling porous media are divided into two main groups, direct measurements by some equipment and stochastic statistical methods. Direct measurements of pore structure can be acquired with current equipment such as X-ray computed tomography and laser scanning confocal microscopy, but the unavailability of the equipment and the high cost of the measurement make their widespread application impossible. Many stochastic statistical methods, such as truncated Gaussian random field and simulated annealing methods, reconstruct 3D porous media based on some 2D thin sections by means of lower-order statistical functions. However these functions cannot reproduce the long-range connectivity of pore structure. Therefore, this paper will present a stochastic technique of reconstructing 3D pore space using multiple-point statistics with the purpose of solving the proposed problems. The single normal equation simulation algorithm (SNESIM), one of the most common methods for discrete variable simulation in multiple-point statistics, is the main tool to reproduce the long-range feature of pore space. To test the method, Berea sandstone was used as a sample. In the simulation process, a 2D thin section was taken as the training image for providing patterns of pore structure and some pixels were extracted from it as the conditioning data. The models were reconstructed using the SNESIM algorithm that serves as the simulation engine. In order to test the accuracy of these reconstructed models, pore geometry and topology and transport properties of the reconstructed models were compared with those of the real model obtained by X-ray computed tomography scanning. The comparison result shows that the reconstructed models are good agreement with the real model obtained by X-ray computed tomography scanning in the two-point correlation function, the pore space features and single- and two-phase flow permeabilities, which verifies that the long-range connectivity of pore space can be reproduced by this method. Comparing with other stochastic methods, a more accurate stochastic technique of reconstructing 3D porous media is put forward when only some 2D thin sections are available.
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