ARL Distinguished Seminar Series

There is consensus in the scientific community that increased levels of greenhouse gases contribute to recent trends in global warming and dramatic changes in weather patterns. Geologic sequestration by injection of CO2 into deep brine aquifers and reservoirs represents one of the most promising approaches for reducing atmospheric CO2. The basis for this potential is the huge global storage capacity existing in geologic formations (primarily deep saline aquifers) and the availability and close proximity of potential injection sites to power generation plants. However, such injections pose significant technical issues in efforts to ensure safety, to minimize leakage probability on a time scale of hundreds or even thousands of years, and to gain public acceptance.

A key goal of our work is to produce a prototypical computational system to accurately predict the fate of injected CO2 in conditions governed by multiphase flow, rock mechanics, multi-component transport, thermodynamic phase behavior, chemical reactions within both the fluid and the rock, and the coupling of all these phenomena over multiple time and spatial scales. Even small leakage rates over long periods of time can unravel the positive effects of sequestration. This effort requires high accuracy in the physical models and their corresponding numerical approximations. For example, an error of one percent per year in a simulation may be of little concern when dealing with CO2 oil recovery flooding, but such an inaccuracy for sequestration will lead to significantly misleading results that could fail to produce any long-term predictive capability. It is important to note that very few parallel commercial and/or research software tools exist for simulating complex processes such as coupled multiphase flow with chemical transport and geomechanics.

In order to address this challenge a robust reservoir simulator comprised of coupled programs that together account for multicomponent, multiscale, multiphase flow and transport through porous media and through wells and that incorporate uncertainty and include robust solvers is required. The coupled programs must be able to treat different physical processes occurring simultaneously in different parts of the domain, and for computational accuracy and efficiency, should also accommodate multiple numerical schemes. In addition, this problem solving environment or framework must have parameter estimation and optimal control capabilities. We present a "wish list" for simulator capabilities as well as describe the methodology employed in the IPARS software being developed at The University of Texas at Austin.