RUI: Predicting Transport Through Heterogeneous Facies Assemblages: Geostatistical Anatomy of Buried-Valley Aquifers

RUI：通过非均质相组合预测输运：埋谷含水层的地统计解剖

• 批准号: 9305285
• 负责人: Robert Ritzi
• 金额: $ 7.6万
• 依托单位: Wright State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-06-15 至 1995-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9305285&HistoricalAwards=false
• 关键词: RUI; Predicting; Transport; Through; Heterogeneous

The inability to sufficiently characterize the spatial distribution of hydraulic conductivity (K) is an obstacle to constructing reliable predictive models of ground-water flow and pollutant transport, to remediating ground-water contamination, and to effectively managing and protecting ground-water resources. In glacially derived aquifers, it is common to have low-K facies (e.g. till or lacustrine clay) juxtaposed with high-K facies (e.g. sand and gravel outwash). In the buried-valley aquifers of the western and central plains of North America, assemblages of permeable outwash facies form large and productive regional aquifer systems. Although the exact geometry of the high-K and low-K facies can only be determined by a prohibitive amount of subsurface information, conditional indicator simulation provides a way to compute a number of realizations of facies distributions that have identical spatial covariance structure and that honor available data. Running transport simulations using a large number of these realizations, and examining the central tendency and variance of simulated contaminant travel times, or the simulated contaminant pathways, can give insight into how contaminants will move through the real system. However, conditional indicator simulations require that an adequate indicator variogram model of the spatial covariance of the facies assemblage can be defined from field data, when in fact the field data are often insufficient for this purpose. The objective of the proposed research under the Research at Undergraduate Institutions Program is to determine variogram models from field data at a number of locations in a buried-valley aquifer system. Two undergraduate students will be involved in field work that will create the basis for their senior research projects. One M.S. student will be involved in the geostatistical analysis of the data. The conceptual model of a specific facies assemblage will be combined with the variogram model to crate a quantitative hydrofacies model. As we begin to catalogue quantitative facies models, we will compare them for similarities and begin to test the hypothesis that one or more archetypal quantitative hydrofacies can be developed for general types of facies assemblages in buried-valley aquifer systems.

无法充分描述空间分布 水力传导系数（K）的降低是建造的障碍 可靠的地下水流和污染物预测模型 运输、补救地下水污染以及 有效管理和保护地下水资源。 在 在冰川含水层中，通常存在低K相（例如， 冰碛或湖相粘土）与高钾相（如砂 和砾石沉积物）。 在西部的埋藏谷含水层中， 和北美中部平原，渗透性 冰水沉积相形成了大型的多产区域含水层系统。 虽然高钾和低钾相的精确几何形状可以 仅由地下禁止量决定 信息，条件指标模拟提供了一种方法， 计算相分布的多个实现， 相同空间协方差结构和可用的荣誉 数据 运行运输模拟使用大量的这些 实现，并检查集中趋势和方差 模拟污染物传播时间，或模拟污染物 途径，可以深入了解污染物如何通过 真实的系统。 然而，条件指标模拟 需要一个适当的空间指标变差函数模型， 相组合的协方差可以从现场数据定义， 而实际上，现场数据往往不足以满足这一要求 目的. 研究建议的研究目的 在本科院校计划是确定变差函数 根据埋藏谷中多个位置的野外数据建立模型 含水层系统 两名本科生将参与 实地考察，将为他们的高级研究奠定基础 项目 一个MS学生将参与地统计 数据分析。 特定相的概念模型 组合将与变差函数模型相结合， 定量水文相模型 当我们开始 定量相模型，我们将比较它们的相似性 并开始测试一个或多个原型 定量水文相可以为一般类型的 埋藏谷含水层系统中的相组合。