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Slug tests offer an efficient method for estimating the hydraulic parameters of an aquifer without water pumping. Two inverse methods are typically used to assess the slug test data and derive parameter estimates of a confined aquifer. The first method provides estimates of both hydraulic conductivity and specific storage, is visual (hence difficult to automate), and is based on the transient-flow analytical solution of Cooperet al.The second method, proposed by Hvorslev, is very straightforward, but provides only hydraulic conductivity estimates. In this study, we are testing the recently proposed quasi-steady method of Koussis and Akylas, which enables the estimation of both hydraulic parameters and, furthermore, can be easily implemented in computer code or an electronic spreadsheet. This quasi-steady method was coupled with the shuffled complex evolution optimization method to fully automate parameter estimation. This coupling is tested using data from field observations, synthetic data produced from the transient-flow analytical solution, and synthetic data with noise. The results show the usefulness and the limitations of the proposed method.Editor Z.W. Kundzewicz [ABSTRACT FROM AUTHOR]

In the great majority of slug tests performed in wells fully penetrating confined geologic formations, and for over-damped conditions, the response data are evaluated with the transient-flow model of Cooper et al. (1967) when the radial hydraulic conductivity Kr and the coefficient of specific storage Ss are to be estimated. That particular analytical solution, however, is computationally involved and awkward to use. Thus, groundwater professionals often use a few pre-prepared type-curves to fit the data by a rough matching procedure, visually or computationally. On the other hand, the method of Hvorslev (1951), which assumes the flow to be quasi-steady, is much simpler but yields only Kr estimates. In this work, we develop a complete quasi-steady flow model that includes a storage balance inside the aquifer and allows estimating Kr and Ss simultaneously, through matching of the well response data to a type-curve. The new model approximates the model of Cooper et al. closely and has the practical advantage that its solution type-curves are generated easily using an electronic spreadsheet, so that the optimal fit of data by a type-curve can be readily automated. [ABSTRACT FROM AUTHOR]