Simulating spatial and temporal varying CO2 signals from sources at the seafloor to help designing risk-based monitoring programs

Abstract

Risk-based monitoring requires quantification of the probability of the design to detect the potentially adverse events. A component in designing the monitoring program will be to predict the varying signal caused by an event, here detection of a gas seep through the seafloor from an unknown location. The Bergen Ocean Model (BOM) is used to simulate dispersion of CO2 leaking from different locations in the North Sea, focusing on temporal and spatial variability of the CO2 concentration. It is shown that the statistical footprint depends on seep location and that this will have to be accounted for in designing a network of sensors with highest probability of detecting a seep. As a consequence, heterogeneous probabilistic predictions of CO2 footprints should be available to subsea geological CO2 storage projects in order to meet regulations.