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Gaussian Process Emulators for Quantifying Uncertainty in CO2 Spreading Predictions in Heterogeneous Media
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.ORCID iD: 0000-0002-9417-5586
School of Mathematics and Statistics, University of Sheffield, UK.
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
Faculty of Engineering, University of Nottingham, UK.
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2017 (English)In: Computers & Geosciences, ISSN 0098-3004, E-ISSN 1873-7803Article in journal (Other academic) Accepted
Abstract [en]

We explore the use of Gaussian process emulators (GPE) in the numerical simulation of CO2 injection into a deep heterogeneous aquifer. The model domain is a two-dimensional, log-normally distributed stochastic permeability field. We first estimate the cumulative distribution functions (CDFs) of the CO2 breakthrough time and the total CO2 mass using a computationally expensive Monte Carlo (MC) simulation. We then show that we can accurately reproduce these CDF estimates with a GPE, using only a small fraction of the computational cost required by traditional MC simulation. In order to build a GPE that can predict the simulator output from a permeability field consisting of 1000s of values, we use a truncated Karhunen-Loève (K-L) expansion of the permeability field, which enables the application of the Bayesian functional regression approach. We perform a cross-validation exercise to give an insight of the optimization of the experiment design for selected scenarios: we find that it is sufficient to use 100s values for the size of the training set and that it is adequate to use as few as 15 K-L components. Our work demonstrates that GPE with truncated K-L expansion can be effectively applied to uncertainty analysis associated with modeling of multiphase flow and transport processes in heterogeneous media.

Place, publisher, year, edition, pages
2017.
Keyword [en]
CO2, Bayesian, Permeability, KL expansion, Monte Carlo, Cumulative distribution function, Uncertainty analysis
National Category
Geosciences, Multidisciplinary
Identifiers
URN: urn:nbn:se:uu:diva-298748DOI: 10.1016/j.cageo.2017.04.006OAI: oai:DiVA.org:uu-298748DiVA: diva2:947102
Funder
EU, FP7, Seventh Framework Programme, 227286EU, FP7, Seventh Framework Programme, 282900EU, FP7, Seventh Framework Programme, 309067
Available from: 2016-07-06 Created: 2016-07-06 Last updated: 2017-05-02Bibliographically approved
In thesis
1. CO2 storage in deep saline aquifers: Models for geological heterogeneity and large domains
Open this publication in new window or tab >>CO2 storage in deep saline aquifers: Models for geological heterogeneity and large domains
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[zh]
二氧化碳的深部盐水层地质封存 : 储层非均质性及大尺度模型的研究
Abstract [en]

This work presents model development and model analyses of CO2 storage in deep saline aquifers. The goal has been two-fold, firstly to develop models and address the system behaviour under geological heterogeneity, second to tackle the issues related to problem scale as modelling of the CO2 storage systems can become prohibitively complex when large systems are considered.

The work starts from a Monte Carlo analysis of heterogeneous 2D domains with a focus on the sensitivity of two CO2  storage performance measurements, namely, the injectivity index (Iinj) and storage efficiency coefficient (E), on parameters characterizing heterogeneity. It is found that E and Iinj are determined by two different parameter groups which both include correlation length (λ) and standard deviation (σ) of the permeability. Next, the issue of upscaling is addressed by modelling a heterogeneous system with multi-modal heterogeneity and an upscaling scheme of the constitutive relationships is proposed to enable the numerical simulation to be done using a coarser geological mesh built for a larger domain. Finally, in order to better address stochastically heterogeneous systems, a new method for model simulations and uncertainty analysis based on a Gaussian processes emulator is introduced. Instead of conventional point estimates this Bayesian approach can efficiently approximate cumulative distribution functions for the selected outputs which are CO2 breakthrough time and its total mass. After focusing on reservoir behaviour in small domains and modelling the heterogeneity effects in them, the work moves to predictive modelling of large scale CO2  storage systems. To maximize the confidence in the model predictions, a set of different modelling approaches of varying complexity is employed, including a semi-analytical model, a sharp-interface vertical equilibrium (VE) model and a TOUGH2MP / ECO2N model. Based on this approach, the CO2 storage potential of two large scale sites is modelled, namely the South Scania site, Sweden and the Dalders Monocline in the Baltic Sea basin.

The methodologies developed and demonstrated in this work enable improved analyses of CO2 geological storage at both small and large scales, including better approaches to address medium heterogeneity. Finally, recommendations for future work are also discussed.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 70 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1390
Keyword
CO2, Carbon Capture Storage, Storage Capacity, Injectivity, Monte Carlo, Gaussian, Permeability, Upscaling, 二氧化碳, 地質封存, 高斯仿真, 滲透係數, 非均質性, 升尺度, 存儲效能, 場地模擬, 不確定性, 壓力累積
National Category
Geosciences, Multidisciplinary
Identifiers
urn:nbn:se:uu:diva-279382 (URN)978-91-554-9625-8 (ISBN)
Public defence
2016-09-16, Hamberg, Villavägen 16, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2016-08-24 Created: 2016-03-01 Last updated: 2016-10-12

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