Fine-grained sandstones, siltstones, and shales have become increasingly important to satisfy the ever-growing global energy demands. Of particular current interest are shale rocks, which are mudstones made up of organic and inorganic constituents of varying pore sizes. These materials exhibit high heterogeneity, low porosity, varying chemical composition and low pore connectivity. Due to the complexity and the importance of such materials, many experimental, theoretical and computational efforts have attempted to quantify the impact of rock features on fluids diffusivity and ultimately on permeability. In this study, we introduce a stochastic kinetic Monte Carlo approach developed to simulate fluid transport. The features of this approach allow us to discuss the applicability of 2D vs 3D models for the calculation of transport properties. It is found that a successful model should consider realistic 3D pore networks consisting of pore bodies that communicate via pore throats, which however requires a prohibitive amount of computational resources. To overcome current limitations, we present a rigorous protocol to stochastically generate synthetic 3D pore networks in which pore features can be isolated and varied systematically and individually. These synthetic networks do not correspond to real sample scenarios but are crucial to achieve a systematic evaluation of the pore features on the transport properties. Using this protocol, we quantify the contribution of the pore network’s connectivity, porosity, mineralogy, and pore throat width distribution on the diffusivity of supercritical methane. A sensitivity analysis is conducted to rank the significance of the various network features on methane diffusivity. Connectivity is found to be the most important descriptor, followed by pore throat width distribution and porosity. Based on such insights, recommendations are provided on possible technological approaches to enhance fluid transport through shale rocks and equally complex pore networks. The purpose of this work is to identify the significance of various pore network characteristics using a stochastic KMC algorithm to simulate the transport of fluids. Our findings could be relevant for applications that make use of porous media, ranging from catalysis to radioactive waste management, and from environmental remediation to shale gas production.

中文翻译：

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一种随机评估孔隙网络几何、化学和拓扑对流体输送影响的新型建模方法

细粒砂岩、粉砂岩和页岩对于满足不断增长的全球能源需求变得越来越重要。目前特别感兴趣的是页岩，它是由不同孔径的有机和无机成分组成的泥岩。这些材料表现出高异质性、低孔隙率、不同的化学成分和低孔隙连通性。由于此类材料的复杂性和重要性，许多实验、理论和计算工作都试图量化岩石特征对流体扩散率和最终对渗透率的影响。在这项研究中，我们引入了一种用于模拟流体传输的随机动力学蒙特卡罗方法。这种方法的特点使我们能够讨论 2D 与 3D 模型在计算传输特性方面的适用性。发现成功的模型应该考虑由通过孔喉进行通信的孔体组成的真实 3D 孔隙网络，但这需要大量的计算资源。为了克服当前的限制，我们提出了一个严格的协议来随机生成合成 3D 孔隙网络，其中孔隙特征可以被系统地和单独地隔离和变化。这些合成网络并不对应于真实的样本场景，但对于实现对传输特性的孔隙特征的系统评估至关重要。使用该协议，我们量化了孔隙网络的连通性、孔隙度、矿物学和孔喉宽度分布对超临界甲烷扩散率的贡献。进行敏感性分析以对各种网络特征对甲烷扩散率的重要性进行排序。连通性是最重要的描述子，其次是孔喉宽度分布和孔隙度。基于这些见解，提供了关于可能的技术方法的建议，以增强通过页岩和同样复杂的孔隙网络的流体输送。这项工作的目的是使用随机 KMC 算法来模拟流体传输，以确定各种孔隙网络特征的重要性。我们的发现可能与利用多孔介质的应用相关，从催化到放射性废物管理，从环境修复到页岩气生产。其次是孔喉宽度分布和孔隙度。基于这些见解，提供了关于可能的技术方法的建议，以增强通过页岩和同样复杂的孔隙网络的流体输送。这项工作的目的是使用随机 KMC 算法来模拟流体传输，以确定各种孔隙网络特征的重要性。我们的发现可能与利用多孔介质的应用相关，从催化到放射性废物管理，从环境修复到页岩气生产。其次是孔喉宽度分布和孔隙度。基于这些见解，提供了关于可能的技术方法的建议，以增强通过页岩和同样复杂的孔隙网络的流体输送。这项工作的目的是使用随机 KMC 算法来模拟流体传输，以确定各种孔隙网络特征的重要性。我们的发现可能与利用多孔介质的应用相关，从催化到放射性废物管理，从环境修复到页岩气生产。这项工作的目的是使用随机 KMC 算法来模拟流体传输，以确定各种孔隙网络特征的重要性。我们的发现可能与利用多孔介质的应用相关，从催化到放射性废物管理，从环境修复到页岩气生产。这项工作的目的是使用随机 KMC 算法来模拟流体传输，以确定各种孔隙网络特征的重要性。我们的发现可能与利用多孔介质的应用相关，从催化到放射性废物管理，从环境修复到页岩气生产。