An inverse method for estimating thickness and volume with time of a thin CO2-filled layer at the Sleipner Field, North Sea

Cowton, L.R.; Neufeld, J.A.; White, N.J.; Bickle, M.J.; White, J.C.; Chadwick, R.A.. 2016 An inverse method for estimating thickness and volume with time of a thin CO2-filled layer at the Sleipner Field, North Sea. Journal of Geophysical Research: Solid Earth, 121 (7). 5068-5085.

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Migration of CO 2 through storage reservoirs can be monitored using time lapse seismic reflection surveys. At the Sleipner Field, injected CO 2 is distributed throughout nine layers within the reservoir. These layers are too thin to be seismically resolvable by direct measurement of the separation between reflections from the top and bottom of each layer. Here we develop and apply an inverse method for measuring thick ness changes of the shallowest layer. Our approach combines differences in traveltime down to a specific reflection together with amplitude measurements to determine layer thicknesses from time lapse surveys. A series of synthetic forward models were used to test the robustness of our inverse approach and to quantify uncertainties. In the absence of ambient noise, this approach can unambiguously resolve layer thickness. If a realistic ambient noise distribution is included, layer thicknesses of 1–6 m are accurately retrieved with an uncertainty of ±0.5 m. We used this approach to generate a thickness map of the shallowest layer. The fidelity of this result was tested using measurements of layer thickness determined from the 2010 broadband seismic survey. The calculated volume of CO 2 within the shallowest layer increases at a rate that is quadratic in time, despite an approximately constant injection rate into the base of the reser voir. This result is consistent with a diminished growth rate of the areal extent of underlying layers. Finally, the relationship between caprock topography and layer thickness is explored and potential migration pathways that charge this layer are identified.

Item Type: Publication - Article
Digital Object Identifier (DOI):
ISSN: 21699313
Date made live: 28 Jul 2016 14:57 +0 (UTC)

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