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| Titel |
Probabilistic risk assessment for CO2 storage in geological formations: robust design and support for decision making under uncertainty |
| VerfasserIn |
Sergey Oladyshkin, Holger Class, Rainer Helmig, Wolfgang Nowak |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250034598
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| Zusammenfassung |
| CO2 storage in geological formations is currently being discussed intensively as a technology
for mitigating CO2 emissions. However, any large-scale application requires a thorough
analysis of the potential risks. Current numerical simulation models are too expensive for
probabilistic risk analysis and for stochastic approaches based on brute-force repeated
simulation. Even single deterministic simulations may require parallel high-performance
computing. The multiphase flow processes involved are too non-linear for quasi-linear error
propagation and other simplified stochastic tools. As an alternative approach, we propose a
massive stochastic model reduction based on the probabilistic collocation method. The
model response is projected onto a orthogonal basis of higher-order polynomials to
approximate dependence on uncertain parameters (porosity, permeability etc.) and design
parameters (injection rate, depth etc.). This allows for a non-linear propagation
of model uncertainty affecting the predicted risk, ensures fast computation and
provides a powerful tool for combining design variables and uncertain variables
into one approach based on an integrative response surface. Thus, the design task
of finding optimal injection regimes explicitly includes uncertainty, which leads
to robust designs of the non-linear system that minimize failure probability and
provide valuable support for risk-informed management decisions. We validate
our proposed stochastic approach by Monte Carlo simulation using a common 3D
benchmark problem (Class et al. Computational Geosciences 13, 2009). A reasonable
compromise between computational efforts and precision was reached already with
second-order polynomials. In our case study, the proposed approach yields a significant
computational speedup by a factor of 100 compared to Monte Carlo simulation.
We demonstrate that, due to the non-linearity of the flow and transport processes
during CO2 injection, including uncertainty in the analysis leads to a systematic and
significant shift of predicted leakage rates towards higher values compared with
deterministic simulations, affecting both risk estimates and the design of injection
scenarios. This implies that, neglecting uncertainty can be a strong simplification for
modeling CO2 injection, and the consequences can be stronger than when neglecting
several physical phenomena (e.g. phase transition, convective mixing, capillary forces
etc.).
The authors would like to thank the German Research Foundation (DFG) for financial
support of the project within the Cluster of Excellence in Simulation Technology (EXC
310/1) at the University of Stuttgart.
Keywords: polynomial chaos; CO2 storage; multiphase flow; porous media; risk
assessment; uncertainty; integrative response surfaces |
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