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Simulating spatial and temporal varying CO 2 signals from sources at the seafloor to help designing risk‐based monitoring programs
Author(s) -
Ali Alfatih,
Frøysa Håvard G.,
Avlesen Helge,
Alendal Guttorm
Publication year - 2016
Publication title -
journal of geophysical research: oceans
Language(s) - English
Resource type - Journals
eISSN - 2169-9291
pISSN - 2169-9275
DOI - 10.1002/2015jc011198
Subject(s) - subsea , seafloor spreading , footprint , petroleum seep , environmental science , probabilistic logic , statistical power , event (particle physics) , temporal scales , false alarm , computer science , geology , methane , oceanography , statistics , artificial intelligence , paleontology , ecology , physics , mathematics , quantum mechanics , biology
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 CO 2 leaking from different locations in the North Sea, focusing on temporal and spatial variability of the CO 2 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 CO 2 footprints should be available to subsea geological CO 2 storage projects in order to meet regulations.