How to Find Aquifer Statistics Utilizing Pumping Tests? Two Field Studies Using welltestpy

Area of Science:

• Hydrogeology
• Geostatistics
• Environmental Science

Background:

• Pumping tests are crucial for understanding aquifer properties.
• Standard analysis methods (e.g., Theis' equation) are limited to mean transmissivity and storativity.
• Geostatistical parameters of aquifer heterogeneity are often overlooked.

Purpose of the Study:

• To present a workflow for estimating geostatistical aquifer parameters from pumping test data.
• To demonstrate the application of the welltestpy Python package for advanced pumping test analysis.
• To go beyond standard methods by inferring parameters like log-transmissivity variance and horizontal correlation length.

Main Methods:

• Utilized the welltestpy Python package for pumping test analysis.
• Applied a semi-analytical drawdown solution based on the Radial Coarse Graining upscaling approach.
• Performed type-curve analysis to estimate aquifer heterogeneity parameters.

Main Results:

• Successfully estimated mean transmissivity, storativity, log-transmissivity variance, and horizontal correlation length from pumping test data.
• Demonstrated the workflow's effectiveness on two German field data sets.
• A sensitivity study highlighted the impact of observation well placement on parameter estimation.

Conclusions:

• The presented workflow offers a comprehensive approach to pumping test analysis, enabling the estimation of geostatistical aquifer properties.
• The welltestpy package provides a versatile tool for advanced hydrogeological investigations.
• Insights gained can optimize future field test designs for geostatistical aquifer characterization.