Uncertainty in static CO2 storage capacity estimates: Case study from the North Sea, UK. Issue 3 (29th May 2013)

• Record Type: Journal Article
• Authors: Hedley, Benjamin J.; Davies, Richard J.; Mathias, Simon A.; Hanstock, David; Gluyas, Jon G.
• Is Part Of: Greenhouse gases. Volume 3:Issue 3(2013:Jun.)
• Page Start: 212
• Page End: 230
• Publication Date: 2013-05-29
• Subjects: carbon capture and storage -- efficiency factors -- uncertainty -- storage -- capacity estimation; Greenhouse gases -- Periodicals; Greenhouse gas mitigation -- Periodicals; 363.7387405
• DOI: 10.1002/ghg.1343
• Languages: English
• ISSNs: 2152-3878

Abstract

Abstract: We used a sub‐salt Rotliegend Group sandstone saline aquifer in the North Sea as a case study site for Monte‐Carlo‐based CO2 geostorage capacity assessment. In the area of interest, this unit is characterized by sparse, low resolution, subsurface data typical of the margins of global petroleum provinces, favored for CO2 storage. Such data scarcity leads to uncertainty regarding the complex trap geometries and ultimate CO2 storage capacity. The Rotliegend reservoir, estimated to have porosity and permeability ranges of 11–27% and 0.2 mD–125 mD, respectively, is sealed by Zechstein salt. The salt, predominantly halite, is a proven hydrocarbon seal in the central and southern North Sea hosting oil and gas columns of >140 m (>450 ft) and >150 m (>500 ft). Utilizing 2D‐seismic data, boreholes and analogues, we estimate the pore volume of a 5‐km 2 4‐way dip‐closed structure through Monte‐Carlo‐based capacity simulations. We estimated storage capacity using published methodologies and compared this against a theoretical total storage calculation analogous to the gas in place equation used in the petroleum industry. We found that different methods yield a capacity range of <10 4 to >10 9 tonnes CO2 where sensitivity analysis indicates variability in reservoir properties to be the dominant control. Thus static estimates based upon Monte‐Carlo calculations present no advantage over theoretical pore volume estimations. This leaves 3D dynamic modeling of storage capacity populated by 3D seismic data and direct down‐hole measurement of reservoir properties to improve confidence in capacity estimations as the recommended method.