A statistical learning approach to model the uncertainties in reservoir quality for the assessment of CO2 storage performance in the lower Permian Rotliegend Group in the Mid North Sea High Area
File(s)
Published version
Author(s)
Govindan, R
Elahi, N
Korre, A
Durucan, S
Hanstock, D
Type
Conference Paper
Abstract
It has been identified that the Rotliegend sandstone reservoir in the Mid North Sea High region, in the UK Quadrants 27-29, has a large-scale CO 2 storage potential of national importance. In this paper, the authors develop a reservoir model using extensive datasets available from seismic interpretations and core analysis. An advanced statistical learning approach was applied to characterise the uncertainties in the spatial distribution of reservoir quality. The model was used to assess the CO 2 injection performance and the preliminary results obtained thusfar indicate promise in the available storage capacities.
Date Issued
2017-08-18
Date Acceptance
2016-11-14
Citation
Energy Procedia, 2017, 114, pp.4637-4642
ISSN
1876-6102
Publisher
Elsevier
Start Page
4637
End Page
4642
Journal / Book Title
Energy Procedia
Volume
114
Copyright Statement
© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Sponsor
Natural Environment Research Council (NERC)
Engineering & Physical Science Research Council (EPSRC)
Grant Number
NE/H01392X/1
EP/K035967/1
Source
13th International Conference on Greenhouse Gas Control Technologies, GHGT-13
Publication Status
Published
Start Date
2016-11-14
Finish Date
2016-11-18
Coverage Spatial
Lausanne, Switzerland