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The AAPG/Datapages Combined Publications Database

Environmental Geosciences (DEG)

Abstract

DOI:10.1306/eg.06140404025

Modeling aquifer heterogeneity using cone penetration testing data and stochastic upscaling methods

Gregory P. Flach,1 Mary K. Harris,2 Andrew D. Smits,3 Frank H. Syms4

1Savannah River National Laboratory, Savannah River Site, 773-42A, Aiken, South Carolina 29808; [email protected]
2Savannah River National Laboratory, Savannah River Site, 773-42A, Aiken, South Carolina 29808
3Science Applications International Corporation, 360 Bay Street, Suite 200, Augusta, Georgia 30901
4Bechtel Savannah River Inc., Savannah River Site, 730-2B, Aiken, South Carolina 29808

AUTHORS

Greg Flach earned a Ph.D. in mechanical engineering from North Carolina State University in 1988. Since then, he has worked at the Savannah River National Laboratory on a variety of environmental and nuclear engineering topics, specializing in mathematical and numerical analysis and simulation.

Mary Harris is the geosciences manager for the Environmental Sciences and Technology Department at the Savannah River National Laboratory. She received her M.S. degree in geology from the University of Idaho and her Ph.D. in geological sciences from the University of South Carolina. One of her fundamental beliefs is that understanding subsurface geology provides the base for successful remedial technology deployments.

Andrew Smits received a B.A. degree in physics and an M.S. degree in geology from the University of North Carolina at Wilmington. Smits is employed as a geologist for Science Applications International Corporation, where he has worked extensively on subsurface characterization and hydrogeologic modeling of the coastal-plain sedimentary sequence beneath the Savannah River Site.

Frank Syms is an engineering geologist for Bechtel, specializing in subsurface exploration in coastal-plain environments. He has a B.S. and M.S. degrees and a Ph.D. from the University of South Carolina. Research interests include geologic interpretation of cone penetration data and sequence stratigraphy reconstruction in the Carolina coastal plain.

ACKNOWLEDGMENTS

We are sincerely grateful to Westinghouse Savannah River Company LLC and the U.S. Department of Energy for making this work possible and for the permission to publish our findings under Contract No. DE-AC09-96SR18500.

ABSTRACT

Cone penetration testing (CPT) has become an increasingly popular characterization method for subsurface investigations under 60 m (200 ft) depth in the Atlantic coastal plain of South Carolina. The shallow Tertiary sediments consist primarily of interbedded and interfingering fluvial, deltaic, and shallow-marine sediments. Cone penetration testing is relatively inexpensive and does not require disposal of drilling fluid or cuttings. At the Savannah River Site, CPT is typically used to obtain depth-discrete groundwater samples and small-diameter permeability samples, and to define hydrostratigraphic horizons. The focus of this study is an environmental waste site where CPT at 139 locations was used to define contaminant plumes and hydrostratigraphy over an 8-km2 (3-mi2) area, instead of conventional borehole techniques (e.g., monitoring wells, cores, electric well logs, slug, and pumping tests). This investigation used the CPT lithologic data to predict hydraulic conductivity variations in hydrostratigraphic zones of the uppermost aquifer unit. The method developed involves correlating tip resistance, sleeve resistance, and pore-pressure measurements to fines (mud, silt, and clay) content and hydraulic conductivity. Predicted fines content at the scale of the CPT measurements (0.03 m; 0.1 ft) are then categorized into high, medium and low conductivity and upscaled to the flow model resolution using a geostatistical approach. The resulting model conductivity field provides a realistic representation of aquifer heterogeneity in coastal-plain sediments and significantly improves groundwater modeling predictions. Subsequent contaminant transport simulations compare well with field data.

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