基于分形理论的致密油藏孔隙结构与分类评价研究

IF 2.3 4区 地球科学
ShiJie Li, HuiYuan Bian, Di Zhang, YanXin Liu, GuoLiang Liu, Fei Wang
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引用次数: 0

摘要

位于鄂尔多斯盆地元城地区的延长组长八油层是中国典型的致密油藏。该储层具有孔隙度低、渗透率低、非均质性强等特点,储层参数评价难度大。为了考察和研究长 8 地层的微观孔隙结构特征,本研究采用了铸造薄片和扫描电镜技术,并对岩心物性进行了测试,将测试数据纳入储层岩石矿物学基本特征、孔隙渗透率和其他基本特征的分析中。系统研究了压汞曲线的形状,以研究 17 个样本的孔隙结构特征和特点。根据核磁共振 T2 波谱的分形维度,将储层分为四类,并通过应用分段幂函数方法,建立了核磁共振 T2 波谱与毛细管压力曲线之间相关性的转换模型。该模型在核磁共振测井解释中得到了应用,有助于获得横跨整个井段的无缝伪毛细管压力曲线。提取了反映微观孔隙结构的三个基本参数,即岩心样品的排出压力、中值压力和分选系数。然后通过应用广义回归神经网络确定储层参数与储层分类之间的关联。通过对整个井段的伪毛细管压力曲线储层参数进行处理,得出储层的分类轮廓,分类结果与注汞实验结果非常吻合。这项研究表明,所提出的方法可为致密油藏孔隙结构研究和储层分类评价提供重要依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Research on pore structure and classification evaluation of tight oil reservoirs based on fractal theory

Research on pore structure and classification evaluation of tight oil reservoirs based on fractal theory

The Chang 8 formation of the Yanchang Group, located in Yuancheng area of the Ordos Basin, is a typical tight oil reservoir in China. This reservoir is characterized by low porosity, low permeability, strong non-homogeneity, and significant difficulty in evaluating the reservoir parameters. To examine and investigate the microscopic pore structure characteristics of the Chang 8 formation, cast thin section and scanning electron microscopy techniques were utilized in this study, Moreover, tests on the core physical properties were conducted and the data from these tests were integrated into the analysis of basic characteristics of the reservoir rock mineralogy, pore permeability, and other fundamental characteristics. The shapes of piezomercury curves were systematically examined to study the characteristics and features of pore structures for the 17 samples. In accordance with the fractal dimension of the NMR T2 spectrum, the reservoir was classified into four categories, and a conversion model delineating the correlation between the NMR T2 spectrum and the capillary pressure curve was formulated through the application of the segmented power function method. This model was then implemented in the interpretation of NMR logging, facilitating the acquisition of a seamless pseudo-capillary pressure curve spanning the entire well section. Three essential parameters reflecting the microscopic pore structure, namely the expulsion pressure, median pressure, and sorting coefficient of the core samples, were extracted. The association between reservoir parameters and reservoir categorization was then determined through the application of a generalized regression neural network. The pseudo-capillary pressure curve reservoir parameters of the whole well section were processed to derive the classification profile of the reservoir, and the classification results demonstrated a strong alignment with those of the mercury injection experiments. This study highlights that the proposed method can provide crucial foundation for the investigations on pore structures in tight oil reservoirs and the evaluation of reservoir classification.

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来源期刊
Acta Geophysica
Acta Geophysica GEOCHEMISTRY & GEOPHYSICS-
CiteScore
3.80
自引率
13.00%
发文量
251
期刊介绍: Acta Geophysica is open to all kinds of manuscripts including research and review articles, short communications, comments to published papers, letters to the Editor as well as book reviews. Some of the issues are fully devoted to particular topics; we do encourage proposals for such topical issues. We accept submissions from scientists world-wide, offering high scientific and editorial standard and comprehensive treatment of the discussed topics.
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