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Pore Properties in Organic-Rich Shales Derived Using Multiple Fractal Determination Models Applied to Two Indian Permian Basins
Energy & Fuels ( IF 5.2 ) Pub Date : 2021-09-02 , DOI: 10.1021/acs.energyfuels.1c02077
Deependra Pratap Singh 1, 2 , David A. Wood 3 , Bodhisatwa Hazra 1, 2 , Pradeep K. Singh 1
Affiliation  

The pore geometry of shale reservoirs plays an important role in the storage and transportation of petroleum in these unconventional resources. Intrinsic surface roughness, indicated by fractal dimensions and contrasting pore size distributions of shales are primary factors that influence shale’s fluid flow and resource volume characteristics. The Frenkel–Halsey–Hill (FHH) derived fractal dimensions are widely applied for evaluating the pore structural complexities of shales. In this work, for a suite of shales samples collected from two Permian basins, India, with distinct maturities, low pressure nitrogen gas adsorption–desorption has been employed to elucidate the pore structural framework. Three alternative fractal calculation methods (FHH, Neimark, and Wang–Li) are compared to provide further insight into the fractal characteristics of the shales. Ambiguities are revealed in the selection of the most appropriate fractal values. Fractal dimensions calculated by the FHH and Wang–Li methods are in relatively close agreement (ranging between 2.5 and 2.8) and the small systematic differences between their values can be explained in terms of fitting errors and assumptions associated with both methods. However, the Neimark fractal values are unreasonable (>3 for all the samples). Thermal maturity is a key controlling feature of the pore structural facets and fractal dimensions of the Raniganj basin samples (Tmax = 431–463 °C) but not for North Karanpura (Tmax = 434–442 °C) samples. Strong correlations exist between calculated fractal dimensions and specific surface area, pore volume, and average pore radius for all samples. However, systematic differences in these values between the samples from the two basins are most likely explained in terms of their mineralogical differences, specifically the higher kaolinite content of the Raniganj samples.

中文翻译:

使用应用于两个印度二叠纪盆地的多重分形确定模型推导出的富有机质页岩的孔隙特性

页岩储层的孔隙几何结构在这些非常规资源中的石油储运中起着重要作用。由分形维数和页岩对比孔径分布表示的内在表面粗糙度是影响页岩流体流动和资源量特征的主要因素。Frenkel-Halsey-Hill (FHH) 派生的分形维数被广泛应用于评估页岩的孔隙结构复杂性。在这项工作中,对于从印度二叠纪盆地收集的一组具有不同成熟度的页岩样品,低压氮气吸附-解吸已被用于阐明孔隙结构框架。三种可选的分形计算方法(FHH、Neimark、和 Wang-Li) 进行比较,以进一步了解页岩的分形特征。在选择最合适的分形值时会发现歧义。FHH 和 Wang-Li 方法计算的分形维数相对接近(范围在 2.5 和 2.8 之间),它们值之间的微小系统差异可以用与这两种方法相关的拟合误差和假设来解释。然而,Neimark 分形值是不合理的(所有样本 >3)。热成熟度是 Raniganj 盆地样品孔隙结构面和分形维数的关键控制特征(FHH 和 Wang-Li 方法计算的分形维数相对接近(范围在 2.5 和 2.8 之间),它们值之间的微小系统差异可以用与这两种方法相关的拟合误差和假设来解释。然而,Neimark 分形值是不合理的(所有样本 >3)。热成熟度是 Raniganj 盆地样品孔隙结构面和分形维数的关键控制特征(FHH 和 Wang-Li 方法计算的分形维数相对接近(范围在 2.5 和 2.8 之间),它们值之间的微小系统差异可以用与这两种方法相关的拟合误差和假设来解释。然而,Neimark 分形值是不合理的(所有样本 >3)。热成熟度是 Raniganj 盆地样品孔隙结构面和分形维数的关键控制特征(T max = 431–463 °C)但不适用于 North Karanpura(T max = 434–442 °C)样品。所有样品的计算分形维数与比表面积、孔体积和平均孔半径之间存在很强的相关性。然而,两个盆地样品之间这些值的系统差异最有可能用它们的矿物学差异来解释,特别是 Raniganj 样品的高岭石含量较高。
更新日期:2021-09-16
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