分形储层复杂性类型的智能提取及其对估算输运特性的意义

Fractals Pub Date : 2024-04-20 DOI:10.1142/s0218348x24500701
YI JIN, BEN ZHAO, YUNHANG YANG, JIABIN DONG, HUIBO SONG, YUNQING TIAN, JIENAN PAN
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引用次数: 0

摘要

分形孔隙结构广泛存在于天然储层中,并主导着储层的输运特性。因此,越来越多的人致力于研究这种复杂的多尺度系统中的传质控制机制。显然,有效表征分形结构至关重要。虽然新出现的复杂性组装概念阐明了分形系统中的复杂性类型及其组装机制,但复杂性类型的等效提取是有效表征的关键。为此,我们提出了一种基于深度学习的方法,以提取床堆积分形多孔介质中的原始复杂性和行为复杂性组装,从而简化并不失一般性。具体而言,通过训练 UNeXt 网络模型来获取不同尺度缩放对象的独立连接区域,利用边缘检测和聚类分析算法来提取连续两个缩放对象之间的数量-尺寸关系,并将数量-尺寸模型和分形地形学结合起来实现分形行为的独特反演。因此,基于复杂性组装的概念,建立了分形复杂孔隙结构的等效表征方法。我们的研究为分形多孔介质的定量表征提供了理论指导和方法参考,为准确评价天然储层的输运特性提供了基本保障。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
INTELLIGENT EXTRACTION OF COMPLEXITY TYPES IN FRACTAL RESERVOIR AND ITS SIGNIFICANCE TO ESTIMATE TRANSPORT PROPERTY

Fractal pore structure exists widely in natural reservoir and dominates its transport property. For that, more and more effort is devoted to investigate the control mechanism on mass transfer in such a complex and multi-scale system. Apparently, effective characterization of the fractal structure is of fundamental importance. Although the newly emerged concept of complexity assembly clarified the complexity types and their assembly mechanism in a fractal system, equivalent extraction of the complexity types is the key for effective characterization. For these, we proposed a deep learning-based method to extract the original and behavioral complexity assembled in bed-packing fractal porous media for simplification and without loss of generality. In detail, the UNeXt network model was trained to obtain the independent connected regions of scaling objects with different scales, the edge detection and clustering analysis algorithms were employed to extract the number-size relationship between two successive scaling objects, and the unique inversion of fractal behavior was realized by taking the number-size model and fractal topography together. Consequently, an equivalent characterization method for fractal complex pore structure was developed based on the concept of complexity assembly. Our investigation provides a theoretical guidance and method reference for the quantitative characterization of fractal porous media that will guarantee the fundamental requirement for the accurate evaluation of the transport properties of natural reservoir.

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