H195-0021
Where to find aquifer statistics utilizing pumping tests? Two field studies using welltestpy.
Where to find aquifer statistics utilizing pumping tests? Two field studies using welltestpy.
Wednesday, 16 December 2020
Poster
Abstract:
This presentation addresses the ability to estimate geo-statistical aquifer parameters from standard pumping test data.
We analyse two field campaigns, namely at the Horkheimer Insel and Lauswiesen site, Germany, with the Python package welltestpy. We present a reproducible workflow to infer parameters of heterogeneity as well as their sensitivity which can serve as template for any pumping test campaigns.
The analysis is based on the upscaling approach Radial Coarse Graining, allowing to infer parameters such as log-transmissivity variance and horizontal correlation length from drawdown data. This goes beyond the possibilities of standard methods, e.g. based on Theis' equation which are limited to mean transmissivity and storativity. Type-curves analyse is used to determine regions of parameter sensitivity, e.g. to identify weaknesses in the well setup. The insights of this study help to optimize future test setups for geo-statistical aquifer analysis.
The welltestpy package allows to easily investigate field sites and to determine simple prior knowledge about their heterogeneity statistics.
We analyse two field campaigns, namely at the Horkheimer Insel and Lauswiesen site, Germany, with the Python package welltestpy. We present a reproducible workflow to infer parameters of heterogeneity as well as their sensitivity which can serve as template for any pumping test campaigns.
The analysis is based on the upscaling approach Radial Coarse Graining, allowing to infer parameters such as log-transmissivity variance and horizontal correlation length from drawdown data. This goes beyond the possibilities of standard methods, e.g. based on Theis' equation which are limited to mean transmissivity and storativity. Type-curves analyse is used to determine regions of parameter sensitivity, e.g. to identify weaknesses in the well setup. The insights of this study help to optimize future test setups for geo-statistical aquifer analysis.
The welltestpy package allows to easily investigate field sites and to determine simple prior knowledge about their heterogeneity statistics.