复杂碳酸盐岩岩心驱替复合岩心构建新途径

Y. Cinar, Ahmed Zayer, Naseem Dawood, D. Krinis
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引用次数: 0

摘要

碳酸盐岩储集层由复杂的孔隙结构和网络组成,形成了多种沉积相。考虑到这种复杂性,我们提出了一种更好地选择岩心驱油复合材料的新方法。在这种方法中,储层桥塞在被分类为储层岩石类型之前,要经过几个实验室测试的彻底过滤过程。这些测试包括常规岩心分析(CCA)、液体渗透率、堵头计算机断层扫描(CT)、核磁共振(NMR)、末端压汞毛细管压力(MICP)、x射线衍射(XRD)、薄层分析(TS)、扫描电子显微镜(SEM)和排水毛细管压力(Pc)。我们建议从一个大的插头池开始,在他们完成筛选过程的不同阶段时缩小选择范围。CT扫描有助于排除具有复合行为的桥塞或含有可能影响岩心注水结果的空洞和裂缝。之后,将桥塞分为不同的组,代表可用的储层岩石类型。然后,我们研究每种岩石类型,并确定所选塞是否具有相似的孔隙结构、岩石纹理和矿物含量。末端装饰MICP通常有助于聚类具有相似孔喉尺寸分布的桥塞。然而,它也带来了挑战,因为它可能不能代表整个桥塞,特别是对于非均质碳酸盐。在这种情况下,我们建议利用NMR功能来验证孔隙大小分布。在孔隙尺寸分布验证后,利用岩石学数据(XRD、TS和SEM)进一步筛选桥塞的结构和矿物相似性。最后,在确定复合材料选择之前,我们评估了Pc的盐水渗透率(Kb)、不可还原水饱和度(Swir)和Swir (Koe,未保存桥塞的润湿性恢复后)的有效油渗透率数据的相似性。本文展示了将该方法应用于碳酸盐岩储层岩石样品的重要方面。在确定岩心驱油实验的最佳组合时,它综合了地质学、岩石物理学和油藏工程因素。通过实践这一工作流程,我们还观察到不同数据源的岩石类型存在相当大的差异,这表明与更具代表性的全塞MICP和NMR测试结果相比,谨慎使用碳酸盐岩的末端数据是可取的。此外,我们通常观察到Kb和Koe渗透率通常低于Klinkenberg渗透率,并且具有不同程度的桥塞特异性,这突出了将这些测量结果作为碳酸盐岩储层岩心注水组合选择的附加标准的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Approach for Building Composite Cores for Corefloods in Complex Carbonate Rocks
Carbonate reservoir rocks are composed of complex pore structures and networks, forming a wide range of sedimentary facies. Considering this complexity, we present a novel approach for a better selection of coreflood composites. In this approach, reservoir plugs undergo a thorough filtration process by completing several lab tests before they get classified into reservoir rock types. Those tests include conventional core analysis (CCA), liquid permeability, plug computed tomography (CT), nuclear magnetic resonance (NMR), end-trim mercury injection capillary pressure (MICP), X-ray diffraction (XRD), thin-section analysis (TS), scanning electron microscopy (SEM), and drainage capillary pressure (Pc). We recommend starting with a large pool of plugs and narrowing down the selection as they complete different stages of the screening process. The CT scans help to exclude plugs exhibiting composite-like behavior or containing vugs and fractures that potentially influence coreflood results. After that, the plugs are categorized into separate groups representing the available reservoir rock types. Then, we look into each rock type and determine whether the selected plugs share similar pore-structures, rock texture, and mineral content. The end-trim MICP is usually helpful in clustering plugs having similar pore-throat size distributions. Nevertheless, it also poses a challenge because it may not represent the whole plug, especially for heterogeneous carbonates. In such a case, we recommend harnessing the NMR capabilities to verify the pore-size distribution. After pore-size distribution verification, plugs are further screened for textural and mineral similarity using the petrographic data (XRD, TS, and SEM). Finally, we evaluate the similarity of brine permeability (Kb), irreducible water saturation (Swir) from Pc, and effective oil permeability data at Swir (Koe, after wettability restoration for unpreserved plugs) before finalizing the composite selection. The paper demonstrates significant aspects of applying the proposed approach to carbonate reservoir rock samples. It integrates geology, petrophysics, and reservoir engineering elements when deciding the best possible composite for coreflood experiments. By practicing this workflow, we also observe considerable differences in rock types depending on the data source, suggesting that careful use of end-trim data for carbonates is advisable compared to more representative full-plug MICP and NMR test results. In addition, we generally observe that Kb and Koe are usually lower than the Klinkenberg permeability with a varying degree that is plug-specific, highlighting the benefit of incorporating these measurements as additional criteria in coreflood composite selection for carbonate reservoirs.
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