Raghavendra Suribhatla

Identification of Heterogeneous Aquifer Transmissivity Using an AE-Based Method

Determination of hydraulic head, H, as a function of spatial coordinates and time, in ground water flow is the basis for aquifer management and for prediction of contaminant transport. Several computer codes are available for this purpose. Spatial distribution of the transmissivity, T(x,y), is a required input to these codes. In most aquifers, T varies in an erratic manner, and it can be characterized statistically in terms of a few moments: the expected value, the variance, and the variogram. Knowledge of these moments, combined with a few measurements, permits one to estimate T at any point using geostatistical methods. In a review of transmissivity data from 19 unconsolidated aquifers, Hoeksema and Kitanidis (1985) identified two types of the logtransmissivity Y= ln(T) variations: correlated variations with variance sigma2Yc and correlation scale, I(Y), on the order of kilometers, and uncorrelated variations with variance sigma2Yn. Direct identification of the logtransmissivity variogram, Gamma(Y), from measurements is difficult because T data are generally scarce. However, many head measurements are commonly available. The aim of the paper is to introduce a methodology to identify the transmissivity variogram parameters (sigma2Yc, I(Y), and sigma2Yn) using head data in formations characterized by large logtransmissivity variance. The identification methodology uses a combination of precise numerical simulations (carried out using analytic element method) and a theoretical model. The main objective is to demonstrate the application of the methodology to a regional ground water flow in Eagle Valley basin in west-central Nevada for which abundant transmissivity and head measurements are available.

Identification of heterogeneous aquifer transmissivity using an AE-based method

Determination of hydraulic head, H, as a function of spatial coordinates and time, in ground water flow is the basis for aquifer management and for prediction of contaminant transport. Several computer codes are available for this purpose. Spatial distribution of the transmissivity, T(x,y), is a required input to these codes. In most aquifers, T varies in an erratic manner, and it can be characterized statistically in terms of a few moments: the expected value, the variance, and the variogram. Knowledge of these moments, combined with a few measurements, permits one to estimate T at any point using geostatistical methods. In a review of transmissivity data from 19 unconsolidated aquifers, Hoeksema and Kitanidis (1985) identified two types of the logtransmissivity Y= ln(T) variations: correlated variations with variance sigma2Yc and correlation scale, I(Y), on the order of kilometers, and uncorrelated variations with variance sigma2Yn. Direct identification of the logtransmissivity variogram, Gamma(Y), from measurements is difficult because T data are generally scarce. However, many head measurements are commonly available. The aim of the paper is to introduce a methodology to identify the transmissivity variogram parameters (sigma2Yc, I(Y), and sigma2Yn) using head data in formations characterized by large logtransmissivity variance. The identification methodology uses a combination of precise numerical simulations (carried out using analytic element method) and a theoretical model. The main objective is to demonstrate the application of the methodology to a regional ground water flow in Eagle Valley basin in west-central Nevada for which abundant transmissivity and head measurements are available.

Statistical analysis of head and transmissivity in natural aquifers: application to structure identification and transport prediction

Flow and transport in natural aquifers depend on the spatially variable transmissivity T, usually modeled as a space random function. As a consequence, all the derived quantites, like piezometric head H, are random functions, and their statistical moments depend on those of T. The present work introduces a methodology for identification of logtransmissivity statistics that use the measured piezometric heads. In particular, second order moments like the head-logtransmissivity cross-covariance CHY and the head variogram ΓH can be conveniently used for parameter inference. The logtransmissivity statistics is characterized by three parameters: the variance σYc and the integral scale IY of correlated residuals, and a nugget σ2Yn that represents variability over small support. Unlike previous studies that pursue the same objective through a first order approximation in the logtransmissivity variance, the emphasis here is on highly heterogeneous formations, characterized by large values of σ2Yc. Under the proposed methodology, indentifications is achieved by fitting the experimental head variograms with the theoretical ones; the latter are derived by the means of the effective medium approximation and are validated through accurate numerical simulations, based on analytic element method. The methodology is applied to the Eagle Valley basin in the west-central Nevada for which numerous transmissivity and head measurements are available. The values of σ2Yc and σ2Yn computed directly, from logtransmissivity data, agreed well with the values computed by fitting head variograms. A disparity prevails for the integral scale IY. A prediction of the solute transport behavior of the aquifer is then made from the inferred data and the theoretical results of -xbDagan et al. [4]. Comparison is also made with results based on the first-order analysis of flow and transport.