Fast estimation of dense gas dispersion from multiple continuous CO2 surface leakage sources for risk assessment

Abstract

Surface leakage of CO2, and associated potential impacts on health, safety, and the environment (HSE) are considered hazards of geologic carbon sequestration (GCS). There are two challenges associated with impact assessment of CO2 surface dispersion. First, the fact that CO2 is a dense gas makes its dispersion in air a complex process. Rigorous numerical solutions for modeling concentration distributions are relatively time-consuming. Second, impact assessment requires consideration of uncertainty, e.g., quantification of how much uncertainty is propagated through input parameters to model outputs by carrying out large numbers of model runs. In order to assess the potential consequences of surface leakage of CO2, it is useful to have a model that executes very quickly for repeated model calculations (e.g., in Monte Carlo mode) of the atmospheric dispersion of CO2 (concentrations as a function of space and time). In addition, the model should be able to handle multiple surface leakage sources. In this study, we have extended the nomograph approach of Britter and McQuaid (1988) for estimating dense gas plume length from single leakage source to multiple leakage sources. The method is very fast and therefore amenable to general system-level GCS risk assessment including uncertainty quantification within the framework of the National Risk Assessment Partnership (NRAP) Integrated Assessment Model (IAM). The method is conservative in that it assumes the wind could be from any direction, and it handles multiple sources by a simple superposition approach. The method produces results in reasonable agreement with a sophisticated computational fluid dynamics (CFD) code, but runs in a small fraction of the time.