Estimating permeability in shales and other heterogeneous porous media: Deterministic vs. stochastic investigations
Estimating permeability in shales and other heterogeneous porous media: Deterministic vs. stochastic investigations
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DOI：
10.1016/j.coal.2019.02.009
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发表时间：
2019-03
影响因子：
5.6
通讯作者：
M. Apostolopoulou;R. Dusterhoft;R. Day;M. Stamatakis;M. Coppens;A. Striolo
中科院分区：
文献类型：
--
作者：
M. Apostolopoulou;R. Dusterhoft;R. Day;M. Stamatakis;M. Coppens;A. Striolo
随着全球能源需求的增长，非常规地层（如页岩）正在成为天然气的重要来源。广泛的努力集中在理解流体的复杂行为（包括它们在地下的运输），以最大限度地提高天然气产量。页岩是由不同孔径（1-500 nm）的有机和无机成分组成的泥岩。利用先进的成像技术，可以在不同的长度尺度上获得页岩的详细结构和化学描述。使用这些知识来评估宏观性质，如流体渗透性，仍然具有挑战性。渗透率的直接实验测量提供了答案，但在时间和资源的成本增加。为了补充这些，计算机模拟是广泛可用的，但是，他们采用显着的近似，和一个可靠的方法来估计渗透率在非均质孔隙网络仍然难以捉摸。在这项研究中，通过实施两种确定性方法和一种随机方法，使用动态蒙特卡罗算法，渗透率预测进行了比较。这种分析的重点是从孔径分布，微孔和大孔的影响，以及各向异性（诱导或自然发生）对预测的基质渗透率的影响。在考虑多个案例研究的同时，就每种方法的最佳使用条件提出了建议。最后，进行随机分析，以估计渗透率的鹰福特页岩样品使用动态蒙特卡罗算法。对实验数据的成功比较表明，所提出的随机方法的吸引力。
With increasing global energy demands, unconventional formations, such as shale rocks, are becoming an important source of natural gas. Extensive efforts focus on understanding the complex behavior of fluids (including their transport in the sub-surface) to maximize natural gas yields. Shale rocks are mudstones made up of organic and inorganic constituents of varying pore sizes (1-500 nm). With cutting-edge imaging technologies, detailed structural and chemical description of shale rocks can be obtained at different length scales. Using this knowledge to assess macroscopic properties, such as fluid permeability, remains challenging. Direct experimental measurements of permeability supply answers, but at elevated costs of time and resources. To complement these, computer simulations are widely available; however, they employ significant approximations, and a reliable methodology to estimate permeability in heterogeneous pore networks remains elusive. For this study, permeability predictions obtained by implementing two deterministic methods and one stochastic approach, using a kinetic Monte Carlo algorithm, are compared. This analysis focuses on the effects resulting from pore size distribution, the impact of micro- and macropores, and the effects of anisotropy (induced or naturally occurring) on the predicted matrix permeability. While considering multiple case studies, recommendations are provided on the optimal conditions under which each method can be used. Finally, a stochastic analysis is performed to estimate the permeability of an Eagle Ford shale sample using the kinetic Monte Carlo algorithm. Successful comparisons against experimental data demonstrate the appeal of the stochastic approach proposed.