Interpreting a Pumping Test Influenced by Another Well in an Anisotropic Aquifer

In confined aquifers the influence of neighboring active wells is often neglected when interpreting a pumping test. This can, however, lead to an erroneous interpretation of the pumping test data. This article presents simple methods to evaluate the transmissivity tensor and storativity of an anisotropic confined aquifer when there is an interfering well in the neighborhood of the tested well. Two methods have been developed depending on whether the tested well or the interfering well is the first in operation. These new methods yield better estimates of the hydraulic parameters than when the influence of the interfering well is neglected. These methods have then been used on data obtained from numerical models with an interfering well and the results have been compared to an analytical method that neglects the influence of the interfering well. The methods require knowledge of the pumping rate of the interfering well and the time elapsed since the pumping started in each well. If the interfering well started pumping before the tested well, the method does not require knowledge of the aquifer piezometric level at the beginning of the test, which is often unknown in this case. As for the method without interference, at least three monitoring wells (MWs) are needed, the position of which influences the accuracy of the estimated parameters. Some recommendations concerning MWs position have been given to get more accurate results according to the sought parameter.     

Improved Parameter Resolution with Markov Chain Monte Carlo Simulation of Different Aquifer Tests

Hydraulic testing for aquifer characterization at contaminated sites often includes tests of short duration and of different types, such as slug tests and pumping tests, conducted at different phases of investigation. Tests conducted on a well cluster installed in a single aquifer can be combined in aggregate inverse analysis using an analytical model for groundwater flow near a test well. A genetic algorithm performs parallel search of the parameter space and provides starting parameter values for a Markov chain Monte Carlo simulation to estimate the parameter distribution. This sequence of inverse methods avoids guessing of the initial parameter vector and the often encountered difficult convergence of gradient-based methods and estimates the parameter covariance matrix from a distribution rather than from a single point in the parameter space. Combination of different tests improves the resolution of the estimated aquifer properties and allows an assessment of the uniformity of the aquifer. Estimated parameter correlations and standard deviations are used as relative metrics to distinguish well resolved and poorly resolved parameters. The methodology is demonstrated on example field tests in unconfined and leaky aquifers.