An Efficient Approach for Quantifying the Uncertainty in Groundwater-model Predictions
V. A. Kelley, S. Mishra, 2006. "An Efficient Approach for Quantifying the Uncertainty in Groundwater-model Predictions", Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II, T. C. Coburn, J. M. Yarus, R. L. Chambers
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Distributed-parameter models are increasingly being used to incorporate hydrogeologic uncertainty in predictive analyses of groundwater flow and contaminant transport. Geostatistical techniques have proven to be useful for generating multiple equiprobable realizations of subsurface parameters that are consistent with the available data. However, computational constraints commonly preclude detailed transport calculations with all realizations in a Monte Carlo simulation framework for quantifying the uncertainty in model predictions. As a result, several studies have been performed investigating techniques to rank realizations of a stochastic reservoir or groundwater aquifer model. These techniques share the common goal of trying to develop a surrogate measure that will preserve the quantiles of the desired performance measure. This chapter proposes a technique to both rank and weight realizations through the use of a surrogate traveltime measure and to then accurately reproduce the performance-measure statistics with only a few forward simulations. The methodology is demonstrated using a field example from the Waste Isolation Pilot Plant site in New Mexico that is well documented in the groundwater literature.
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Since publication of the first volume of Stochastic Modeling and Geostatistics in 1994, there has been an explosion of interest and activity in geostatistical methods and spatial stochastic modeling techniques. Many of the computational algorithms and methodological approaches that were available then have greatly matured, and new, even better ones have come to the forefront. Advances in computing and increased focus on software commercialization have resulted in improved access to, and usability of, the available tools and techniques. Against this backdrop, Stochastic Modeling and Geostatistics Volume II provides a much-needed update on this important technology. As in the case of the first volume, it largely focuses on applications and case studies from the petroleum and related fields, but it also contains an appropriate mix of the theory and methods developed throughout the past decade. Geologists, petroleum engineers, and other individuals working in the earth and environmental sciences will find Stochastic Modeling and Geostatistics Volume II to be an important addition to their technical information resources.