--> Use of Geostatistical Simulation and Inverse Flow Modeling to Reduce Uncertainty and Facilitate Decision Making Related to Groundwater Contamination Problems, by E. P. Poeter and S. McKenna; #90986 (1994).
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Abstract: Use of Geostatistical Simulation and Inverse Flow Modeling to Reduce Uncertainty and Facilitate Decision Making Related to Groundwater Contamination Problems

Eileen P. Poeter, Sean McKenna

Effective evaluation of groundwater flow and transport problems requires consideration of the range of possible interpretations of the subsurface given the available disparate types of data. Geostatistical simulation of hydrofacies units are used to evaluate the range of subsurface interpretations of unit geometry. Hydraulic observations are used to accept or reject the geometric configurations and to estimate groundwater flow Previous HitparametersNext Hit for the units. These realizations are used to evaluate the uncertainty of the resulting value of the response function (contaminant concentration). These uncertainties are considered in the decision making process related to Previous HitselectingNext Hit disposal sites, prioritizing sites for remedial action, or Previous HitselectingNext Hit from alternative remedial actions for a site. The simulations can result in improper conclusions if the input data are biased, are not fully utilized, or are not properly weighted. In such a case, the addition of

new (but biased) data narrows the apparent uncertainty associated with the analysis, while the estimate of the mean of the response function may be further from the true value. The true value may not be realized within the reduced range of interpretation. A synthetic data set is employed to explore and illustrate these problems, because the "true" field condition must be known in order to demonstrate the strengths and shortcomings of the method. Examples illustrate this situation, and remedies are suggested, including: consideration of a broader range of simulation Previous HitparametersTop; decreasing the sampling bias by use of geologic knowledge and geophysical data; and better constraining the simulations to geologic geometries representative of the depositional setting.

AAPG Search and Discovery Article #90986©1994 AAPG Annual Convention, Denver, Colorado, June 12-15, 1994