We present a workflow to estimate geostatistical aquifer parameters from pumping test data using the Python package welltestpy. The procedure of pumping test analysis is exemplified for two data sets from the Horkheimer Insel site and from the Lauswiesen site, Germany. The analysis is based on a semi-analytical drawdown solution from the upscaling approach Radial Coarse Graining, which enables to infer log-transmissivity variance and horizontal correlation length, beside mean transmissivity, and storativity, from pumping test data. We estimate these parameters of aquifer heterogeneity from type-curve analysis and determine their sensitivity. This procedure, implemented in welltestpy, is a template for analyzing any pumping test. It goes beyond the possibilities of standard methods, for example, based on Theis' equation, which are limited to mean transmissivity and storativity. A sensitivity study showed the impact of observation well positions on the parameter estimation quality. The insights of this study help to optimize future test setups for geostatistical aquifer analysis and provides guidance for investigating pumping tests with regard to aquifer statistics using the open-source software package welltestpy.

中文翻译：

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如何利用抽水测试查找含水层统计数据？使用 welltestpy 进行的两项实地研究

我们提出了一个工作流程，使用 Python 包welltestpy从抽水测试数据中估计地统计含水层参数。抽水测试分析的过程以来自霍克海默因塞尔站点和德国劳斯维森站点的两个数据集为例。该分析基于升级方法径向粗粒度的半解析下降解决方案，该解决方案能够从泵送测试数据推断对数透射率方差和水平相关长度，以及平均透射率和存储量。我们通过类型曲线分析估计含水层异质性的这些参数并确定它们的敏感性。这个过程，在welltestpy中实现, 是分析任何泵送测试的模板。它超越了标准方法的可能性，例如，基于 Theis 方程的标准方法仅限于平均透射率和存储率。敏感性研究表明观测井位置对参数估计质量的影响。本研究的见解有助于优化未来地统计含水层分析的测试设置，并为使用开源软件包welltestpy研究与含水层统计相关的抽水测试提供指导。