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Which geologic characteristics control porosity and permeability in hydrothermal reservoirs?

机译:哪种地质特征控制水热储层中的孔隙率和渗透性?

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Permeability in hydrothermal reservoirs is typically conceptualized as a branching network of variably discontinuous vertical and horizontal pathways. These pathways represent primary and secondary porosity and permeability associated with the stratigraphic section and fault system, respectively. Within this network, there are typically relatively few through-going flow pathways among a multitude of minor and/or dead-end pathways. The through-going pathways allow the vertical movement of fluids from depth to the near-surface, facilitating advective transport of heat. Geothermal production exploits one or more of these through-going flow paths. Still, other through-going flow paths (i.e., under-developed parts of the reservoir) might exist. Here, we present analysis aimed at identifying these under-developed parts of geothermal reservoirs as part of a DOEGTO-funded machine learning project lead by National Renewable Energy Laboratory (NREL). Focusing on the Brady geothermal system in western Nevada, we develop twelve 3D geologic proxies that may control the spatial distribution of through-going flow paths in the reservoir. By using principal component analysis (PCA), we compare the distribution of these proxies to the distribution of porosity and permeability values from an existing, robust, and history-matched reservoir model. PCA reveals which geologic proxies most closely correlate with the distribution of reservoir porosity and permeability values. At Brady, 3D fault density and 3D fault intersection/fault termination density most closely correlate with the permeability distribution in the Brady reservoir model. Spatially mapping the distribution of these geologic characteristics may help improve performance of the reservoir model. These geologic characteristics may also help identify under-developed volumes in this and other hydrothermal reservoirs.
机译:水热储存器中的渗透性通常被概念化为可变不连续垂直和水平途径的分支网络。这些途径分别代表与地层截面和故障系统相关的初级和次要孔隙率和渗透率。在该网络中,多种次要和/或死端通路中通常存在相对较少的流动途径。通转途径允许流体从深度到近表面的垂直运动,促进热的热量。地热量生产利用其中一个或多个这些流动的流动路径。尽管如此,可能存在其他通越流路(即,储存器的不发达的部分)。在这里,我们展示了旨在识别地热水库的这些未发达的地区的分析,是国家可再生能源实验室(NREL)领导的车辆资助机器学习项目的一部分。专注于内华达州西部的布拉迪地热系统,我们开发了12个3D地质代理,可以控制水库中通过流动路径的空间分布。通过使用主成分分析(PCA),将这些代理的分布与现有,鲁棒和历史匹配的储层模型的孔隙度和渗透率值进行比较。 PCA显示哪些地质代理与储层孔隙率和渗透率值最密切相关。在Brady,3D故障密度和3D故障交叉口/故障终止密度与布拉底储层模型中的渗透性分布最密切相关。空间映射这些地质特征的分布可能有助于提高储层模型的性能。这些地质特征还可以有助于识别该水热储层中的不发达的体积。

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