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Record W1967497314 · doi:10.2118/2009-156

Simulation of Trapping Processes for CO2 Storage in Saline Aquifers

2009· article· en· W1967497314 on OpenAlexaboutno aff
Long D. Nghiem, Vijay Shrivastava, Bruce Kohse, Mohamed Hassam, Changbing Yang

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsCitationComputer scienceLibrary scienceOperations researchEngineering

Abstract

fetched live from OpenAlex

Abstract Important modelling techniques for CO2 storage in saline aquifers are discussed, in particular solubility trapping, residual gas trapping and geochemistry for mineral trapping. These modelling techniques are applied to the simulation of several important aspects of CO2 storage, which include optimizing total trapping through water injection, assessing the security of residual trapping with regards to leakage through the cap rocks and evaluating the conversion of CO2 into minerals. Introduction Saline aquifers represent the most important venue for CO2 storage since they have the largest capacity among all other venues (coal seams, depleted gas and oil fields). Table 1 (DOE and NETL, Carbon and Sequestration Atlas for the United States and Canada, 2008) shows the dominance of saline aquifers for large scale deployment of CO2 storage. There are several CO2 trapping mechanisms in saline aquifers:structural trapping;residual gas trapping;solubility trapping andmineral trapping. Structural trapping involves the storage of CO2 in a geological structure as a free gas or super-critical fluid. CO2 can flow and escape through the cap rock or sealing faults if the integrity of the latter is compromised. Residual gas trapping consists of storing CO2 as an immobile gas in the porous media. This process has been identified as one of the most important processes for safe CO2 storage as the immobile gas can be kept away from the cap rock. CO2 is highly soluble in brine and solubility trapping is essentially the impetus for CO2 storage in saline aquifers. As CO2 dissolves in brine, it decomposes into H+ and HCO−3. These ions in turn react with the minerals in place. Depending on the mineralogy of the formation, these reactions could induce precipitation of carbonate minerals such as Calcite, Dolomite and Siderite, which corresponds essentially to the conversion of CO2 into minerals. This paper discusses the modelling of the physics associated with CO2 storage in saline aquifers and illustrates through examples the important storage processes. Table 1: Storage Capacity for CO2 Storage in North America (Available in full paper) CO2 has a critical pressure of 7,376 kPa and a critical temperature of 304.2 K (31 °C). Thus, CO2 is normally a supercritical fluid at aquifer conditions. For convenience, CO2 is referred to as a "gas" in this paper. To be general, the gas phase is treated as a multicomponent mixture. The simulation and modelling techniques described in this paper are based on the geochemical equation-of-state compositional simulator, GEM ™ with the greenhouse gas option (Nghiem et al., 2004). This simulator has been used extensively for modelling CO2 storage in saline aquifers in the past five years. Pruess et al. (2003) and Zhang et al. (2007) describe a simulator for CO2 storage in aquifers with similar features. Solubility Trapping The accurate modeling of gas solubility in the aqueous phase (brine) is important as brine can dissolve a large amount of CO2.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.955
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.278
Teacher spread0.252 · 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

Citations35
Published2009
Admission routes1
Has abstractyes

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