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

Rajesh Govindan; Nasim Elahi; Anna Korre; Sevket Durucan; David Hanstock

doi:10.1016/j.egypro.2017.03.1592

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

Rajesh Govindan^*, Nasim Elahi, Anna Korre, Sevket Durucan, David Hanstock

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

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 CO₂ 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 CO₂ injection performance and the preliminary results obtained thusfar indicate promise in the available storage capacities.

Original language	English
Pages (from-to)	4637-4642
Number of pages	6
Journal	Energy Procedia
Volume	114
DOIs	https://doi.org/10.1016/j.egypro.2017.03.1592
Publication status	Published - 2017
Externally published	Yes
Event	13th International Conference on Greenhouse Gas Control Technologies, GHGT 2016 - Lausanne, Switzerland Duration: 14 Nov 2016 → 18 Nov 2016

Keywords

Rotliegend sandstone
geological uncertainty
regression
saline aquifers

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10.1016/j.egypro.2017.03.1592

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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",

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 CO2 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 CO2 injection performance and the preliminary results obtained thusfar indicate promise in the available storage capacities.",

keywords = "Rotliegend sandstone, geological uncertainty, regression, saline aquifers",

author = "Rajesh Govindan and Nasim Elahi and Anna Korre and Sevket Durucan and David Hanstock",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors.; 13th International Conference on Greenhouse Gas Control Technologies, GHGT 2016 ; Conference date: 14-11-2016 Through 18-11-2016",

year = "2017",

doi = "10.1016/j.egypro.2017.03.1592",

language = "English",

volume = "114",

pages = "4637--4642",

journal = "Energy Procedia",

issn = "1876-6102",

publisher = "Elsevier Ltd",

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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. / Govindan, Rajesh; Elahi, Nasim; Korre, Anna et al.
In: Energy Procedia, Vol. 114, 2017, p. 4637-4642.

Research output: Contribution to journal › Conference article › peer-review

TY - JOUR

T1 - 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

AU - Govindan, Rajesh

AU - Elahi, Nasim

AU - Korre, Anna

AU - Durucan, Sevket

AU - Hanstock, David

PY - 2017

Y1 - 2017

N2 - 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 CO2 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 CO2 injection performance and the preliminary results obtained thusfar indicate promise in the available storage capacities.

AB - 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 CO2 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 CO2 injection performance and the preliminary results obtained thusfar indicate promise in the available storage capacities.

KW - Rotliegend sandstone

KW - geological uncertainty

KW - regression

KW - saline aquifers

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DO - 10.1016/j.egypro.2017.03.1592

M3 - Conference article

AN - SCOPUS:85029666245

SN - 1876-6102

VL - 114

SP - 4637

EP - 4642

JO - Energy Procedia

JF - Energy Procedia

T2 - 13th International Conference on Greenhouse Gas Control Technologies, GHGT 2016

Y2 - 14 November 2016 through 18 November 2016

ER -

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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Keywords

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