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孔隙介质孔隙结构的重建和基于SNESIM算法的多指数模拟效应分析

Qing Xie^1,2, Jiaqi Gao^3,4, Xiaochuang Ye⁵

¹College of Petroleum Engineering, Xi'an Shiyou University, Xi'an, 710065, China. xieqing@xsyu.edu.cn.

Scientific reports

|February 9, 2025

概括

该SNESIM算法有效地重建2D多孔介质毛孔结构,显示出适用于各种毛孔的良好适用性. 对于3D应用和复杂结构,需要进一步的研究.

科学领域:

地质科学地质科学
石油工程是石油工程中的一个.
计算科学计算科学

背景情况:

孔隙结构显著影响多孔介质的透性和流体流动.
准确的孔隙结构重建对于理解这些特性至关重要.
多点统计 (MPS) 方法,包括SNESIM,是孔隙建模的先进技术.

研究的目的:

为了研究SNESIM算法的有效性,从核心切片重建2D孔结构.
从同一个核心分析算法在不同孔径的性能.
确定SNESIM用于孔隙结构建模的局限性和适用条件.

主要方法:

利用CT扫描图像创建数字核心模型.
在2D核心切片上应用SNESIM算法进行毛孔结构重建.
分析重建的图像与训练图像,使用诸如孔隙性和透性等参数进行分析.

主要成果:

SNESIM 显示出良好的适用性和可靠性,重建的图像通常与培训图像趋势相匹配.
性能分析包括毛孔度,毛孔喉比率,平均粒径半径,协调数和透性.
在大多数样本中,算法显示了与训练图像 (TI) 相比,重建图像 (RI) 的趋势类似.

结论:

SNESIM算法是一种可靠的工具,用于在多孔介质中进行二维孔状结构重建.
需要进一步的研究来将SNESIM扩展到3D重建,并处理更复杂的孔隙结构.
为了在不同的地质环境中提高准确性和性能,需要优化SNESIM.

关键词:

协调号码协调号码数字核心数字核心数字核心对于MPS来说,这是一个很好的选择.透性透性的的媒体的媒体变量图表变量图表变量图

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