Events

Dr. Ahmed ElSheikh will give a talk entitled "Bayesian Model Comparison and Uncertainty Quantification of Subsurface Reservoirs"

Abstract: Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans and maximizing recovery from hydrocarbon reservoirs. However, an important distinction has to be made between parameter estimation and model selection. Parameter estimation is a fitting procedure that determines the optimal set of parameters that bring the model closer to the data. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different complexities. We investigate an efficient Bayesian sampling algorithm known as nested sampling, which can simultaneously perform the tasks of parameter estimation, sampling from the posterior distribution for uncertainty quantification, and estimating the Bayesian evidence for model comparison. Nested sampling has the advantage of generality of application, and computational feasibility. In this work, we report the first successful application of nested sampling for calibration and model selection of several nonlinear subsurface flow problems.