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Characterization and estimation of reservoir properties in a carbonate reservoir in Southern Iran by fractal methods

机译:分形方法表征和估算伊朗南部碳酸盐岩储层的储层性质

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Reservoir heterogeneity has a major effect on the characterization of reservoir properties and consequently reservoir forecast. In reality, heterogeneity is observed in a wide range of scales from microns to kilometers. A reasonable approach to study this multi-scale variations is through fractals. Fractal statistics provide a simple way of relating variations on larger scales to those on smaller scales and vice versa. Simple statistical fractal models (fBm and fGn) can be useful to understand the model construction and help the reservoir structure characterization. In this paper, the fractal methods (fGn and fBm) have been applied to characterize and to estimate of reservoir properties. Three methods, namely box-counting, variogram, and R/S analysis, were carried out on log and core data for porosity and permeability data to estimate fractal dimension; a high fractal dimension estimated in this study reveals a high heterogeneity in the reservoir. Moreover, sampling from simulated fractal data at non-existing data depths enables us to generate appropriate realizations of reservoir permeability with suitable accuracy at a proper computational time.
机译:储层非均质性对储层特征的表征以及储层预测具有重要影响。实际上,在从微米到千米的各种尺度上都可以观察到异质性。研究这种多尺度变化的一种合理方法是通过分形。分形统计提供了一种简单的方法,可以将较大规模的变化与较小规模的变化相关,反之亦然。简单的统计分形模型(fBm和fGn)对于理解模型构造并帮助描述储层结构很有用。在本文中,分形方法(fGn和fBm)已用于表征和估算储层性质。对孔隙度和渗透率数据的对数和岩心数据进行盒计数,方差图和R / S分析三种方法,以估计分形维数。在这项研究中估计的高分形维数揭示了储层中的高度非均质性。此外,从不存在的数据深度处的模拟分形数据中采样,使我们能够在适当的计算时间以适当的精度生成适当的储层渗透率实现。

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