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Record W2498135072 · doi:10.2118/148631-pa

Effect of Temperature and Pressure on Contact Angle and Interfacial Tension of Quartz/Water/Bitumen Systems

2011· article· en· W2498135072 on OpenAlexafffund
Maryam Rajayi, Apostolos Kantzas

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2011
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersSharif University of TechnologyCanada Research ChairsPorous Media Laboratory
KeywordsAsphaltSurface tensionContact angleOil sandsDrop (telecommunication)Petroleum engineeringMaterials scienceViscositySurface energyComposite materialGeologyThermodynamics

Abstract

fetched live from OpenAlex

Summary Thermal-recovery methods (e.g., steam injection) are commonly used to recover bitumen from oil sands. The injected steam contacts the oil sand and forms an interface. The steam changes to water, transferring its heat to bitumen across this interface. The heated bitumen will have a lower viscosity, which allows for oil to be mobilized and recovered from the reservoir. Studies that explain hot-water/bitumen interfaces are crucial for understanding thermal-recovery methods. The strength and energy of hot-water/bitumen interfaces are expected to play important roles in the recovery of bitumen from oil sands. However, measurements on hot-water/bitumen interfaces are scarce in the literature. A relevant measurement would be the contact angle and interfacial tension (IFT) of the water/bitumen interfaces at different temperatures. In this paper, it has been attempted to reveal and present the results of several water/bitumen contact-angle and IFT measurements. The measurements cover a temperature range from ambient to 100°C for a given pressure. The experiments are run in X-ray transparent cells, and images are taken using a microcomputed-tomography (microCT) scanner. The results of contact angle and the IFTs of the hot-water/bitumen interface are produced by using the axisymmetric drop shape-analysis (ADSA) method.

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 score0.009
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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

Citations37
Published2011
Admission routes2
Has abstractyes

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