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Data worth of the hydraulic conductivity measurements : slug test and pumping test

University of British Columbia

The high cost of groundwater remediation is directly related to hydrogeological uncertainty. Of several parameters responsible for that uncertainty, hydraulic conductivity (K) is the most important, and at the same time the most difficult to estimate. K can be measured in the lab or field using permeameter tests, piezocone tests, slug tests and pumping tests. However, the hydraulic conductivities measured with these tests are not directly comparable because they characterize different volumes of the subsurface. In practice, one would like to know which method can be used to solve the engineering problem at hand most costeffectively. For example, is it cheaper from the risk-cost-benefit standpoint to take small-scale measurements with slug tests, or larger-scale measurements using pumping tests? Which method will provide greatest reduction in the uncertainty of the hydraulic conductivity field? I focus on the value of the two most commonly used field techniques, the slug test and pumping test, and address the problem using an empirical/numerical approach. First I examine the averaging properties of the pumping test using sensitivity analysis. The pumping-test averaging volume has an elliptical shape, and its size is proportional to the test duration and to the distance between the pumping well and the observation well. The averaging exhibits characteristic zonation, with zones behind and in-between the wells having the strongest impact on the pumping-test scale K. Additionally, the analysis shows the interwell zone influences the pumping-test K for tests of all duration. While the above mentioned properties of a pumping-test averaging volume disintegrate with increasing heterogeneity, some characteristic features can still be distinguished, even for strongly heterogeneous K fields. Next I develop a data-worth methodology applicable to measurements taken at different scales. The method relies upon the representation of larger-scale measured parameters as spatially-averaged smallerscale parameters. It combines a decision model, a hydraulic conductivity uncertainty model, and groundwater flow model employed in a Monte Carlo mode. I apply the data-worth methodology to a generic contamination scenario, where a decision maker is faced with contamination of a 2-D aquifer. The results show that a single pumping-test measurement has higher worth than a single slug-test measurement, and that the worth of a pumping-test measurement increases with increasing distance between the observation well and the pumping well. The worth of two slug-test measurements is comparable with the worth of a small scale pumping-test measurement, however the large scale pumpingtest measurement still proves to be more valuable. The higher data-worth of pumping test suggests that, on sites with configuration similar to the generic scenario, measurements with large averaging volume provide greater reduction in risk, and have greater impact on the decision making process.

Thesis/Dissertation

application/pdf

2009-02-17

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http://hdl.handle.net/2429/4726

Geological Sciences

University of British Columbia

UBCV

DSpace