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Record W2560321470 · doi:10.1021/acs.iecr.6b03729

Mass Transfer of CO<sub>2</sub> in a Carbonated Water–Oil System at High Pressures

2016· article· en· W2560321470 on OpenAlexafffund
Guanli Shu, Mingzhe Dong, Shengnan Chen, Hassan Hassanzadeh

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2016
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilPetroleum Technology Research Centre
KeywordsMass transferDiffusionInterphaseMass transfer coefficientThermodynamicsPhase (matter)Thermal conductionChemistryEnhanced oil recoveryCarbonateHeat transferPetroleum engineeringMaterials scienceChromatographyGeology

Abstract

fetched live from OpenAlex

In this paper, CO 2 diffusion coefficients in a carbonate water–oil system are determined by measuring the pressure buildup in the closed water–oil system experimentally and modeling the pressure change mathematically. The mathematical method of investigating one-dimensional, time-dependent heat conduction in a composite medium is adopted to solve the mass transfer problem between two liquid phases. The model is combined with well-designed trial-and-error method to determine diffusion coefficients of CO 2 in both water and oil phases at the same time. The model considers a moving interface between carbonated water and oil as well as variations of interface concentrations of CO 2 in these two phases, which more effectively conforms to reality. Results show that the pressure buildup during the diffusion process resulted from the increased density and swelling of the oil phase. The diffusion coefficient of CO 2 in the water phase plays a major role in the interphase mass transfer process.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0010.001
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 head0.028
GPT teacher head0.256
Teacher spread0.228 · 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 designBench or experimental
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

Citations46
Published2016
Admission routes2
Has abstractyes

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