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Accounting for Model Uncertainties Using Reliability Methods - Application to Carbon Dioxide Geologic Sequestration System--Geomatrix Consultants, Inc., 2101 Webster Street, 12th Floor, Oakland, CA 94612-3027; 510-663-4100; www.geomatrix.com

Dr. Chin Man Mok, PhD, Principal Investigator, cmmok@geomatrix.com

Mr. Martin Mullins, Business Official, mmullins@geomatrix.com

DOE Grant No. DE-FG02-07ER84932

Amount: $99,976

Numerical modeling is a critical tool for designing effective subsurface management programs, including groundwater remediation and waste repositories. Because constitutive parameters and subsurface-condition variables often are not known to the desired accuracy, the results of model predictions are inherently uncertain; therefore, engineering designs that account for model prediction variability are more reliable and are more likely to achieve performance goals. In recent years, stochastic models have been developed to incorporate model input-output relationships and model input uncertainties into a computational framework, in order to estimate the variability of model predictions. Although these models focus on capturing the mean, variance, and covariance of predicted values, the computed probabilistic measures might not be sufficient for accurately estimating the probability of extreme events. This project will develop a distributed/parallel probabilistic modeling code, CALRELTOUGH, which will utilize reliability methods to incorporate parameter sensitivity and uncertainty analysis into subsurface flow and transport models. The CALRELTOUGH code will be based on the state-of-the-art reliability analysis code, CALREL, and the parallel version of the state-of-the-art subsurface flow and transport code, TOUGH2. For this project, we will apply the CALRELTOUGH model to a carbon dioxide geologic sequestration system, in order to benchmark performance.

Commercial Applications and other Benefits as described by the awardee: The CALRELTOUGH code should enable the efficient and accurate assessment of the uncertainty associated with numerical modeling. It can support the design of subsurface management systems, optimizing costs and reducing the likelihood of negative impacts from extreme events at thousands of government and private sites around the world. The current environmental remediation market is $6.4B annually; the subsurface repository market (e.g., nuclear waste, CO2, natural gas, petroleum, and water) has a similar or even greater market size.