Assessing Transmissivity from Specific Capacity in a Large and Heterogeneous Alluvial Aquifer

Authors

  • M. Razack,

    1. Laboratoire d'Hydrogéologie et U.R.A. 1359; Université des Sciences et Techniques, Place Eugène Bataillon, 34095 Mont-pellier Cedex 5, France.
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    • Moumtaz Razack obtained the degrees of Docteur-Ingenieur in Hydrogeology and Docteur d'Etat from the University of Montpeller in 1978 and 1984, respectively. He is currently Maitre de Conferences at the Universite des Sciences et Techniques. His primary research interests are the structure and hydrogeology of heterogeneous and fissured reservoirs.

  • David Huntley

    1. Department of Geological Sciences, San Diego State University, San Diego, California 92182-0337.
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    • David Huntley has been a Professor of Geological Sciences at San Diego State University since 1978. His research interests include the application of geophysical techniques to hydrogeology, numerical modeling of ground-water flow and solute transport, parameter estimation, and ground-water flow in fractured crystalline rock aquifers.


  • Discussion open until May 1, 1992.

Abstract

Transmissivity is often estimated from specific capacity data because of the expense of conducting standard aquifer tests to obtain transmissivity and the relative availability of specific capacity data. Most often, analytic expressions relating specific capacity to transmissivity derived by Thomasson and others (1960), Theis (1963), or Brown (1963) are used in this analysis. This paper focuses on a test of these relations using a large (215 pairs) data set from a heterogeneous aquifer.

The analytic solutions predicting transmissivity from specific capacity do not agree well with the measured transmissivities, apparently due to turbulent well loss within the production wells, which is not taken into account by any of the analytic solutions. Empirical relations are better than the theoretical relations. Log-log functions have greater correlation coefficients than linear functions. The best relation found for the data set chosen for this study has a correlation coefficient of 0.63, but the prediction interval was about 1.2 log cycles, indicating that the range of probable transmissivities corresponding to a single specific capacity was more than one order of magnitude. Tests with smaller subsets of data suggest that correlations based on data sets of 10 points or less are of limited value.

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