Development of HFU-based permeability prediction models using core data for characterisation of a heterogeneous Oligocene sand in the Nam Con Son basin

Van Hieu Nguyen, Hồng Minh Nguyễn, Ngọc Quốc Phan, H. G. Phạm
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Abstract

Core data by both routine and special core analysis are required to understand and predict reservoir petrophysical characteristics. In this research, a total number of 50 core plugs taken from an Oligocene sand (T30) in the Nam Con Son basin, offshore southern Vietnam, were tested in the core laboratory of the Vietnam Petroleum Institute (VPI). The results of routine core analysis (RCA) including porosity and permeability measurements were employed to divide the study reservoir into hydraulic flow units (HFUs) using the global hydraulic elements (GHEs) approach. Based on five classified HFUs, 16 samples were selected for special core analysis, i.e., mercury injection capillary pressure (MICP) and grain size analyses for establishing non-linear porosity-permeability model of each HFU based on Kozeny-Carman equation, which provides an improved prediction of permeability (R2 = 0.846) comparing to that by the empirical poro-perm relationship (R2 = 0.633). In addition, another permeability model, namely the Winland R35 method, was applied and gave very satisfactory results (R2 = 0.919). Finally, by integrating the results from MICP and grain size analyses, a good trendline of pore size distribution index (λ) and grain sorting was successfully obtained to help characterise the study reservoir. High λ came with poor sorting, and vice versa, the low λ corresponded to good sorting of grain size.
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基于hfu的南con Son盆地渐新统非均质砂岩渗透率预测模型的建立
为了了解和预测储层岩石物性特征,常规岩心分析和特殊岩心分析都需要岩心数据。在这项研究中,在越南石油研究所(VPI)的岩心实验室中,对越南南部海上Nam Con Son盆地渐新世砂岩(T30)的50个岩心桥塞进行了测试。利用常规岩心分析(RCA)结果,包括孔隙度和渗透率测量,采用全局水力单元(GHEs)方法将研究储层划分为水力流量单元(hfu)。在5个分类HFU的基础上,选取16个样品进行特殊岩心分析,即压汞毛细管压力(MICP)和粒度分析,基于Kozeny-Carman方程建立各HFU的非线性孔隙-渗透率模型(R2 = 0.846),与经验孔隙-渗透率关系(R2 = 0.633)相比,该模型对渗透率的预测精度更高(R2 = 0.846)。此外,采用另一种渗透率模型,即Winland R35方法,得到了非常满意的结果(R2 = 0.919)。最后,通过综合MICP和粒度分析结果,成功获得了孔隙尺寸分布指数(λ)和粒度分选的良好趋势线,有助于表征研究储层。λ越高,分选效果越差;反之,λ越低,分选效果越好。
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