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Record W2332022572 · doi:10.1021/ef301549a

Determination of Three-Phase Boundaries of Solvent(s)–CO<sub>2</sub>–Heavy Oil Systems under Reservoir Conditions

2012· article· en· W2332022572 on OpenAlexaff
Huazhou Li, Daoyong Yang, Xiaoli Li

Bibliographic record

VenueEnergy & Fuels · 2012
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPhase diagramSolventPhase (matter)ThermodynamicsChemistryBinary systemVolume (thermodynamics)Binary numberPhase boundaryAnalytical Chemistry (journal)ChromatographyOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

The liquid–liquid–vapor (L 1 L 2 V) phase boundaries of solvent(s)–CO 2 –heavy oil systems under reservoir conditions are experimentally and theoretically determined. Experimentally, the L 1 L 2 V phase boundaries of one CO 2 –heavy oil mixture, one C 3 H 8 –CO 2 –heavy oil mixture, and one n -C 4 H 10 –CO 2 –heavy oil mixture in the pressure–temperature ( P – T ) diagram are determined using a versatile pressure–volume–temperature ( PVT ) setup. The addition of an alkane solvent to the CO 2 –heavy oil system tends to expand the pressure and temperature span of the L 1 L 2 V phase boundary, while the L 1 L 2 V phase boundary of the solvent(s)–CO 2 –heavy oil system shows its tendency to move toward the high-temperature and low-pressure region of the P – T diagram. Theoretically, the previously developed binary interaction parameter (BIP) correlations for CO 2 –heavy oil binary, C 3 H 8 –heavy oil binary, and n -C 4 H 10 –heavy oil binary are incorporated into the Peng–Robinson equation of state (PR EOS) to determine the three-phase boundaries of the above-mentioned systems. The PR EOS with a modified α function and the BIP correlations is found to provide a generally good prediction of the experimentally measured L 1 L 2 V phase boundaries of the solvent(s)–CO 2 –heavy oil systems under reservoir conditions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.736

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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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

Citations38
Published2012
Admission routes1
Has abstractyes

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