Combining multiple lower-fidelity models for emulating complex model responses for CCS environmental risk assessment

Bianchi, Marco; Zheng, Liange; Birkholzer, Jens T.. 2016 Combining multiple lower-fidelity models for emulating complex model responses for CCS environmental risk assessment. International Journal of Greenhouse Gas Control, 46. 248-258. https://doi.org/10.1016/j.ijggc.2016.01.009

Before downloading, please read NORA policies.

Preview

Text
Linkfun_paper_revision_FINAL_CLEAN.pdf - Accepted Version
Download (1MB) | Preview

Official URL: http://dx.doi.org/10.1016/j.ijggc.2016.01.009

Abstract/Summary

Numerical modeling is essential to support natural resource management and environmental policy-making. In the context of CO2 geological sequestration, these models are indispensible parts of risk assessment tools. However, because of increasing complexity, modern numerical models require a great computational effort, which in some cases may be infeasible. An increasingly popular approach to overcome computational limitations is the use of surrogate models. This paper presents a new surrogate modeling approach to reduce the computational cost of running a complex, high-fidelity model. The approach is based on the simplification the high-fidelity model into computationally efficient, lower-fidelity models and on linking them with a mathematical function (linking function) that addresses the discrepancies between outputs from models with different levels of fidelity. The resulting linking function model, which can be developed with small computational effort, can be efficiently used in numerical applications where multiple runs of the original high-fidelity model are required, such as for uncertainty quantification or sensitivity analysis. The proposed approach was then applied to the development of a reduced order model for the prediction of groundwater quality impacts from CO2 and brine leakage for the National Risk Assessment Partnership (NRAP) project.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.ijggc.2016.01.009
ISSN:	17505836
Date made live:	04 Feb 2016 08:49 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/512850

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.ijggc.2016.01.009

ISSN:

17505836

Date made live:

04 Feb 2016 08:49 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/512850