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Record W2321152136 · doi:10.3997/2214-4609.201413374

Numerical Modeling for Assessing the Effects of Thermodynamic Properties on CO2 Storage in Saline Aquifers

2015· article· en· W2321152136 on OpenAlexaboutno aff
Mohsen Pasdar, S.M. Seyyedi Nasooh Abad, Behzad Rostami

Bibliographic record

VenueProceedings · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsAquiferSalinityDissolutionEnvironmental sciencePetroleum engineeringCarbon sequestrationAquifer propertiesSoil scienceGeologyGroundwaterCarbon dioxideGeotechnical engineeringChemistryGroundwater recharge

Abstract

fetched live from OpenAlex

Summary According to the Intergovernmental Panel on Climate Change (IPCC), global CO2 emissions must be reduced by 50 to 80 percent by 2050 to avoid dramatic consequences of global warming. Geological storage of CO2 in saline aquifers is a promising method for reducing atmospheric CO2 concentration. For this aim, accurate modeling of CO2 sequestration into underground formations (saline aquifers) is required. In petroleum industry, normally this is achieved by using compositional reservoir simulators which is computationally expensive and time consuming. To overcome this, an accurate fluid model was coupled to a flow simulator to model CO2 sequestration in saline aquifers. Next, sensitivity analyses of thermodynamic properties (pressure, temperature and salinity) were done on some saline aquifers of Alberta basin, in Canada to investigate the effects of thermodynamics properties on CO2 dissolution in these aquifers. Results show that salinity has the strongest effect on CO2 dissolution in our studied aquifers compared to temperature and pressure effects. Results of this study enable us to assess the potential of each saline aquifer for CO2 storage and therefore help us in selecting suitable injection sites for CO2 sequestration.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.286
Teacher spread0.251 · 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 teacher head, 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

Citations1
Published2015
Admission routes1
Has abstractyes

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