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| Titel |
Finding a balance between accuracy and computational effort for modeling biomineralization |
| VerfasserIn |
Johannes Hommel, Anozie Ebigbo, Robin Gerlach, Alfred B. Cunningham, Rainer Helmig, Holger Class |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250122203
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| Publikation (Nr.) |
 EGU/EGU2016-1177.pdf |
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| Zusammenfassung |
| One of the key issues of underground gas storage is the long-term security of the storage site.
Amongst the different storage mechanisms, cap-rock integrity is crucial for preventing
leakage of the stored gas due to buoyancy into shallower aquifers or, ultimately, the
atmosphere. This leakage would reduce the efficiency of underground gas storage and
pose a threat to the environment. Ureolysis-driven, microbially induced calcite
precipitation (MICP) is one of the technologies in the focus of current research
aiming at mitigation of potential leakage by sealing high-permeability zones in cap
rocks.
Previously, a numerical model, capable of simulating two-phase multi-component
reactive transport, including the most important processes necessary to describe MICP, was
developed and validated against experiments in Ebigbo et al. [2012]. The microbial ureolysis
kinetics implemented in the model was improved based on new experimental findings and the
model was recalibrated using improved experimental data in Hommel et al. [2015]. This
increased the ability of the model to predict laboratory experiments while simplifying some
of the reaction rates.
However, the complexity of the model is still high which leads to high computation times
even for relatively small domains. The high computation time prohibits the use of the model
for the design of field-scale applications of MICP. Various approaches to reduce the
computational time are possible, e.g. using optimized numerical schemes or simplified
engineering models.
Optimized numerical schemes have the advantage of conserving the detailed equations, as
they save computation time by an improved solution strategy. Simplified models are more an
engineering approach, since they neglect processes of minor impact and focus on the
processes which have the most influence on the model results. This allows also for
investigating the influence of a certain process on the overall MICP, which increases the
insights into the interactions of different processes and the relative importance of each
process for the overall MICP. An additional motivation for this approach is that for field
applications, the important input parameters such as porosity and permeability are not known
reliably. In light of this uncertainty related to the input-parameter identification, excessively
detailed equations might be an unnecessary burden to modeling MICP as the overall
reliability of the model predictions already is highly influenced by the uncertainty of these
input parameters.
A. Ebigbo, A.J. Phillips, R. Gerlach, R. Helmig, A.B. Cunningham, H. Class, L.H.
Spangler. Darcy-scale modeling of microbially induced carbonate mineral precipitation in
sand columns. Water Resources Research, 48, (2012)
J. Hommel, E. Lauchnor, A.J. Phillips, R. Gerlach, A. B. Cunningham, R. Helmig, A.
Ebigbo, H. Class. A revised model for microbially induced calcite precipitation -
improvements and new insights based on recent experiments. Water Resources Research, 51,
3695-3715, (2015) |
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