Development of a Reactive Transport Model for Field‐Scale Simulation of Microbially Induced Carbonate Precipitation

Minto, James M.; Lunn, Rebecca; Mountassir, Gráinne El

doi:10.1029/2019wr025153

Cited by 92 publications

(53 citation statements)

References 55 publications

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“…MICP has been proven to reduce permeability and enhance mechanical strength even at large, field-relevant scales (e.g. [33,41,46,56,59]). There are several reviews about the understanding of bio-improved soils (e.g.…”

Section: Biogeochemical Processes In Subsurface Porous Mediamentioning

confidence: 99%

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Surrogate-based Bayesian comparison of computationally expensive models: application to microbially induced calcite precipitation

et al. 2021

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Geochemical processes in subsurface reservoirs affected by microbial activity change the material properties of porous media. This is a complex biogeochemical process in subsurface reservoirs that currently contains strong conceptual uncertainty. This means, several modeling approaches describing the biogeochemical process are plausible and modelers face the uncertainty of choosing the most appropriate one. The considered models differ in the underlying hypotheses about the process structure. Once observation data become available, a rigorous Bayesian model selection accompanied by a Bayesian model justifiability analysis could be employed to choose the most appropriate model, i.e. the one that describes the underlying physical processes best in the light of the available data. However, biogeochemical modeling is computationally very demanding because it conceptualizes different phases, biomass dynamics, geochemistry, precipitation and dissolution in porous media. Therefore, the Bayesian framework cannot be based directly on the full computational models as this would require too many expensive model evaluations. To circumvent this problem, we suggest to perform both Bayesian model selection and justifiability analysis after constructing surrogates for the competing biogeochemical models. Here, we will use the arbitrary polynomial chaos expansion. Considering that surrogate representations are only approximations of the analyzed original models, we account for the approximation error in the Bayesian analysis by introducing novel correction factors for the resulting model weights. Thereby, we extend the Bayesian model justifiability analysis and assess model similarities for computationally expensive models. We demonstrate the method on a representative scenario for microbially induced calcite precipitation in a porous medium. Our extension of the justifiability analysis provides a suitable approach for the comparison of computationally demanding models and gives an insight on the necessary amount of data for a reliable model performance.

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Section: Biogeochemical Processes In Subsurface Porous Mediamentioning

confidence: 99%

“…to mitigate leakages from a geological gas reservoir into above aquifers in advance (e.g. [12,13,35,41,46]). Our limited knowledge about the interaction of the processes that govern biogeochemical systems leads to several modeling approaches that differ, e.g., in their level of detail.…”

Section: Biogeochemical Processes In Subsurface Porous Mediamentioning

confidence: 99%

Surrogate-based Bayesian comparison of computationally expensive models: application to microbially induced calcite precipitation

et al. 2021

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“…The MICP treatment method has gained interest due to its relatively environmentally friendly characteristics, low energy consumption, and sustainable advantages [ 7 ]. Many recent studies have shown that the MICP treatment technique could effectively improve strength and stiffness [ 8 , 9 , 10 , 11 , 12 ]; decrease permeability [ 13 , 14 , 15 , 16 ]; increase resistance to liquefaction [ 17 , 18 , 19 ]; and enhance concrete self-healing [ 20 , 21 , 22 ]. All the above studies indicated that as a new technology for foundation reinforcement, MICP could effectively improve stiffness, strength, and permeability.…”

Section: Introductionmentioning

confidence: 99%

Effects of Different Types of Fibers on the Physical and Mechanical Properties of MICP-Treated Calcareous Sand

et al. 2021

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Microbial-induced calcite precipitation (MICP) has been a promising method to improve geotechnical engineering properties through the precipitation of calcium carbonate (CaCO3) on the contact and surface of soil particles in recent years. In the present experiment, water absorption and unconfined compressive strength (UCS) tests were carried out to investigate the effects of three different fiber types (glass fiber, polyester fiber, and hemp fiber) on the physical and mechanical properties of MICP-treated calcareous sand. The fibers used were at 0%, 0.10%, 0.15%, 0.20%, 0.25%, 0.30%, 0.35%, and 0.40% relative to the weight of the sand. The results showed that the failure strain and ductility of the samples could be improved by adding fibers. Compared to biocemented sand (BS), the water absorption of these three fiber-reinforced biocemented sands were, respectively, decreased by 11.60%, 21.18%, and 7.29%. UCS was, respectively, increased by 24.20%, 60.76%, and 6.40%. Polyester fiber produced the best effect, followed by glass fiber and hemp fiber. The optimum contents of glass fiber and polyester fiber were 0.20% and 0.25%, respectively. The optimum content of hemp fiber was within the range of 0.20–0.25%. Light-emitting diode (LED) microscope and scanning electron microscope (SEM) images lead to the conclusion that only a little calcite precipitation had occurred around the hemp fiber, leading to a poor bonding effect compared to the glass and polyester fibers. It was therefore suggested that polyester fiber should be used to improve the properties of biocemented sand.

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“…One step towards overcoming this difficulty is the numerical model that we provide for EICP. For MICP, numerical models have been shown to be capable of capturing the complex interplay of hydraulics, precipitation reactions, and the change in hydraulic properties [12,[18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Numerical Model for Enzymatically Induced Calcium Carbonate Precipitation

et al. 2020

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Enzymatically induced calcium carbonate precipitation (EICP) is an emerging engineered mineralization method similar to others such as microbially induced calcium carbonate precipitation (MICP). EICP is advantageous compared to MICP as the enzyme is still active at conditions where microbes, e.g., Sporosarcina pasteurii, commonly used for MICP, cannot grow. Especially, EICP expands the applicability of ureolysis-induced calcium carbonate mineral precipitation to higher temperatures, enabling its use in leakage mitigation deeper in the subsurface than previously thought to be possible with MICP. A new conceptual and numerical model for EICP is presented. The model was calibrated and validated using quasi-1D column experiments designed to provide the necessary data for model calibration and can now be used to assess the potential of EICP applications for leakage mitigation and other subsurface modifications.

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Development of a Reactive Transport Model for Field‐Scale Simulation of Microbially Induced Carbonate Precipitation

Cited by 92 publications

References 55 publications

Surrogate-based Bayesian comparison of computationally expensive models: application to microbially induced calcite precipitation

Surrogate-based Bayesian comparison of computationally expensive models: application to microbially induced calcite precipitation

Effects of Different Types of Fibers on the Physical and Mechanical Properties of MICP-Treated Calcareous Sand

A Numerical Model for Enzymatically Induced Calcium Carbonate Precipitation

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