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

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Title: 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
Authors: Govindan, R
Elahi, N
Korre, A
Durucan, S
Hanstock, D
Item 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.
Issue Date: 18-Aug-2017
Date of Acceptance: 14-Nov-2016
URI: http://hdl.handle.net/10044/1/54627
DOI: https://dx.doi.org/10.1016/j.egypro.2017.03.1592
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/).
Sponsor/Funder: Natural Environment Research Council (NERC)
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: NE/H01392X/1
EP/K035967/1
Conference Name: 13th International Conference on Greenhouse Gas Control Technologies, GHGT-13
Publication Status: Published
Start Date: 2016-11-14
Finish Date: 2016-11-18
Conference Place: Lausanne, Switzerland
Appears in Collections:Faculty of Engineering
Earth Science and Engineering



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