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Record W3186054563

Reactive transport modelling of geological storage of CO2 with impurities: lessons learned

2019· article· en· W3186054563 on OpenAlexaff
Dirk Kirste, Julie K. Pearce, S. D. Golding

Bibliographic record

VenueSSRN Electronic Journal · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSolubilityFlue gasPetroleum engineeringCarbon capture and storage (timeline)Environmental scienceChemistryImpurityProcess engineeringChemical engineeringEngineeringGeologyOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

The CO2 captured from the flue gases of coal fired power plants can contain a range of impurities that may impact the chemical properties and the security of a geological storage system. How the storage system can be affected depends on the solubility and reactivity of the particular impurities. Gases with low solubility and/or reactivity can reduce the CO2 storage volume by occupying pore space while soluble and/or reactive gases can result in physical (fluid density) and chemical (redox, acidity) changes that could change the storage capacity and security. The most reactive impurities tend to produce strong acids and therefore are considered to be of concern for storage sites as the strong acids will result in increased interaction with the minerals that make up both the reservoir and seal. This can lead to an increased potential for integrity issues around the well bore and the seal as well as pose a risk to groundwater quality if any leakage occurs. Understanding how the impurities might impact a system is critical to ensuring effective and safe storage and one of the most comprehensive approaches used to make an assessment is through reactive transport modelling (RTM). Reactive transport modelling enables predictive evaluation of the impacts but there are significant uncertainties associated with RTM that need to be addressed before confidence in the modelling can be achieved. In this study, RTM of injection of CO2 with SO2 and CO2 with NO2 and O2 was conducted for a proposed injection and storage site in the Surat Basin in Queensland, Australia. Sensitivity to reactive mineral content, impurity concentration and initial formation water composition as well as mineral reaction rates and reactive surface area was evaluated by generating a series of models. Model outputs were found to be particularly sensitive to the reactive mineral content and impurity concentration in the injection stream. The composition of the initial formation water did not have a significant effect except in cases where the alkalinity was very high and resulted in buffering of the acid producing reactions. In the Surat Basin, salinities tend to be relatively low so the range of salinity of the initial formation water was limited and did not affect injectivity through salt formation during dryout. The presence or absence of carbonates was found to be a critical parameter in determining the extent of pH buffering. Even a very small amount of calcite/siderite/ankerite was sufficient to significantly buffer the very low pH induced by presence of impurities in the CO2 steam and the formation of strong acids. Deciding whether or not a reaction is in equilibrium or controlled by a reaction rate was critical to model output. In particular, rates applied to reactions occurring in solution such as redox reactions or gas solution had significant impacts. Without aqueous phase reaction kinetics, differences in the model outputs tended to be consistent where equilibrium controlled reactions resulted in slightly more extensive dissolution/precipitation in the short time scale of the model runs. With aqueous phase reaction kinetics, models conformed better to observations from P-T-X experiments and resulted in more realistic simulations. Because many of these reactions tend to be proximal to the GHGT-14 injector, discretization also played a role in the output. Controlling uncertainty in RTM was critical to increasing confidence in the models. By reducing the uncertainty in the most sensitive components of the models, less sensitive but often more easily constrained aspects could be focused on and potentially better models would result.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.253
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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