Aquifer‐Parameter Evaluation from Variable‐Rate Pumping Tests Using Convolution and Sensitivity Analysis

Abstract

The reliability of predictive and management models for ground water would be improved by better aquifer‐parameter estimation. As progress continues in the use of computers to simulate ground‐water systems, parallel progress must occur in data collection and analysis. Among the various methods available for the determination of aquifer parameters, pumping tests occupy a prominent position. The maximum advantage is gained from a pumping test when geological knowledge of the aquifer and the analysis of aquifer test data complement each other. This paper presents a technique involving the use of convolution and sensitivity analysis to obtain the “best fit” of aquifer parameters in a least‐squares sense from a pumping test with variable pumping rate. The method also can be used to analyze the residual drawdown data obtained during the recovery period. In addition, this method can also analyze drawdown and recovery data conjunctively. Constant drawdown and variable discharge data of artesian flowing wells also can be analyzed by this method. The method is straightforward, quick, inexpensive, and is always objective. No graphical plots or graphical interpretations are needed. As a measure of error, the rms (root‐mean‐square) error in drawdown is calculated along with the correlation coefficient between pumping‐test data and the theoretically generated data, using the converged values of transmissivity and storage coefficient.

Estimating confined aquifer parameters using a simple derivative-based method

“…The root-mean-square error (rms error) of drawdown (Eq. (13)) is used to compare the estimation accuracy of the proposed method with available methods [30]. …”

Sensitivity analysis of a no-crossflow model for the transient flowmeter test

“…Although this concept is often used in sensitivity analysis (Bishop et al, 1976;McElwee and Yukler, 1978;Morita and Gray, 1981;Cob et al, 1982;Butt and McElwee, 1985;Knopman and Voss, 1987;Shah et al, 1988;Datta-Gupta et al, 1997;Vasco and Datta-Gupta, 1999), it generally does not allow for comparison of the output sensitivities to parameters with different physical units (e.g., hydraulic conductivity and specific storativity) or the comparison of sensitivities of different outputs (e.g., drawdown and flowrate) to even the same parameter. The dimensionless concept of the logarithmic sensitivity…”

On the Reliability of Variable‐Rate Pumping Test Results: Sensitivity to Information Content of the Recorded Data

“…Such reductions in discharge rate during the pumping test may even occur more than 50% of the time in developing countries (Sen & Altunkaynak, 2004). Consequently, since about 1963 (Abu‐Zied & Scott, 1963), there has been an interest in methods that enable aquifer parameters to be estimated using variable‐discharge pumping test data; see, for example, Abu‐Zied and Scott (1963), Hantush (1964), Lai et al (1973) , Butt and McElwee (1985) , Sharma et al (1985) , Rasmussen et al (2003) , Sen and Altunkaynak (2004) , and Zhang (2013).…”

“…Such reductions in discharge rate during the pumping test may even occur more than 50% of the time in developing countries (Sen & Altunkaynak, 2004). Consequently, since about 1963 (Abu-Zied & Scott, 1963), there has been an interest in methods that enable aquifer parameters to be estimated using variable-discharge pumping test data; see, for example, Abu-Zied and Scott (1963), Hantush (1964), Lai et al (1973), Butt and McElwee (1985), Sharma et al (1985), Rasmussen et al (2003), Sen and Altunkaynak (2004), and Zhang (2013). A major feature of all of the aforementioned studies is their dependence on fairly specific, and therefore restrictive, assumptions regarding the functional form of discharge rate variation-the rate is usually assumed to change either linearly or exponentially with time.…”