ABSTRACT The focus of this work is general methods for prioritization or screening of project sites based on the favorability of multiple spatial criteria. We present a threshold-based transformation of each underlying spatial favorability factor into a continuous scale with a common favorability interpretation across all criteria. We compare several methods of computing site favorability and propagating uncertainty from the data to the favorability metrics. Including uncertainty allows decision makers to determine if seeming differences among sites are significant. We address uncertainty using Taylor series approximations and analytical distributions, which are compared to computationally intensive Monte Carlo simulations. Our methods are applied to siting direct-use geothermal energy projects in the Appalachian Basin, where our knowledge about any particular site is limited, yet sufficient data exist to estimate favorability. We consider four factors that contribute to site favorability: the thermal resource described by the depth to 80°C rock, natural reservoir productivity described by rock permeability and thickness, potential for induced seismicity, and the estimated cost of surface infrastructure for heat distribution. Those factors are combined in three ways. We develop favorability uncertainty propagation and sensitivity analysis methods. All methods are general and can be applied to other multi-criteria spatial screening problems.

中文翻译：

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应用于直接利用地热项目的多标准空间筛选和不确定性分析

摘要 这项工作的重点是基于多个空间标准的有利性对项目地点进行优先排序或筛选的一般方法。我们将基于阈值的每个潜在空间有利因素转换为连续尺度，并在所有标准中具有共同的有利解释。我们比较了几种计算站点好感度和将数据中的不确定性传播到好感度指标的方法。包括不确定性允许决策者确定站点之间的表面差异是否显着。我们使用泰勒级数近似和解析分布来解决不确定性，并将其与计算密集型蒙特卡罗模拟进行比较。我们的方法应用于阿巴拉契亚盆地的直接利用地热能源项目选址，我们对任何特定网站的了解有限，但有足够的数据来估计好感度。我们考虑了四个有助于选址有利的因素：由 80°C 岩石深度描述的热资源、由岩石渗透率和厚度描述的天然储层生产力、诱发地震活动的潜力以及用于热分布的地表基础设施的估计成本。这些因素以三种方式结合在一起。我们开发了好感度不确定性传播和敏感性分析方法。所有方法都是通用的，可以应用于其他多标准空间筛选问题。由岩石渗透率和厚度、诱发地震活动的潜力以及用于热分布的地表基础设施的估计成本来描述的天然储层生产力。这些因素以三种方式结合在一起。我们开发了好感度不确定性传播和敏感性分析方法。所有方法都是通用的，可以应用于其他多标准空间筛选问题。由岩石渗透率和厚度、诱发地震活动的潜力以及用于热分布的地表基础设施的估计成本来描述的天然储层生产力。这些因素以三种方式结合在一起。我们开发了好感度不确定性传播和敏感性分析方法。所有方法都是通用的，可以应用于其他多标准空间筛选问题。