Practical Use of Computationally Frugal Model Analysis Methods.

Three challenges compromise the utility of mathematical models of groundwater and other environmental systems: (1) a dizzying array of model analysis methods and metrics make it difficult to compare evaluations of model adequacy, sensitivity, and uncertainty; (2) the high computational demands of many popular model analysis methods (requiring 1000's, 10,000 s, or more model runs) make them difficult to apply to complex models; and (3) many models are plagued by unrealistic nonlinearities arising from the numerical model formulation and implementation. This study proposes a strategy to address these challenges through a careful combination of model analysis and implementation methods. In this strategy, computationally frugal model analysis methods (often requiring a few dozen parallelizable model runs) play a major role, and computationally demanding methods are used for problems where (relatively) inexpensive diagnostics suggest the frugal methods are unreliable. We also argue in favor of detecting and, where possible, eliminating unrealistic model nonlinearities-this increases the realism of the model itself and facilitates the application of frugal methods. Literature examples are used to demonstrate the use of frugal methods and associated diagnostics. We suggest that the strategy proposed in this paper would allow the environmental sciences community to achieve greater transparency and falsifiability of environmental models, and obtain greater scientific insight from ongoing and future modeling efforts.

Model for CO2 leakage including multiple geological layers and multiple leaky wells.

Geological storage of carbon dioxide (CO2) is likely to be an integral component of any realistic plan to reduce anthropogenic greenhouse gas emissions. In conjunction with large-scale deployment of carbon storage as a technology, there is an urgent need for tools which provide reliable and quick assessments of aquifer storage performance. Previously, abandoned wells from over a century of oil and gas exploration and production have been identified as critical potential leakage paths. The practical importance of abandoned wells is emphasized by the correlation of heavy CO2 emitters (typically associated with industrialized areas) to oil and gas producing regions in North America. Herein, we describe a novel framework for predicting the leakage from large numbers of abandoned wells, forming leakage paths connecting multiple subsurface permeable formations. The framework is designed to exploit analytical solutions to various components of the problem and, ultimately, leads to a grid-free approximation to CO2 and brine leakage rates, as well as fluid distributions. We apply our model in a comparison to an established numerical solverforthe underlying governing equations. Thereafter, we demonstrate the capabilities of the model on typical field data taken from the vicinity of Edmonton, Alberta. This data set consists of over 500 wells and 7 permeable formations. Results show the flexibility and utility of the solution methods, and highlight the role that analytical and semianalytical solutions can play in this important problem.

Development of a hybrid process and system model for the assessment of wellbore leakage at a geologic CO2 sequestration site.

Sequestration of CO2 in geologic reservoirs is one of the promising technologies currently being explored to mitigate anthropogenic CO2 emissions. Large-scale deployment of geologic sequestration will require seals with a cumulative area amounting to hundreds of square kilometers per year and will require a large number of sequestration sites. We are developing a system-level model, CO2-PENS, that will predict the overall performance of sequestration systems while taking into account various processes associated with different parts of a sequestration operation, from the power plant to sequestration reservoirs to the accessible environment. The adaptability of CO2-PENS promotes application to a wide variety of sites, and its level of complexity can be increased as detailed site information becomes available. The model CO2-PENS utilizes a science-based-prediction approach by integrating information from process-level laboratory experiments, field experiments/observations, and process-level numerical modeling. The use of coupled process models in the system model of CO2-PENS provides insights into the emergent behavior of aggregate processes that could not be obtained by using individual process models. We illustrate the utility of the concept by incorporating geologic and wellbore data into a synthetic, depleted oil reservoir. In this sequestration scenario, we assess the fate of CO2 via wellbore release and resulting impacts of CO2 to a shallow aquifer and release to the atmosphere.