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EXPERIMENTAL AND SIMULATION STUDIES OF HEAVY OIL/WATER RELATIVE PERMEABILITY CURVES: EFFECT OF TEMPERATURE

2013· article· en· W1996255706 on OpenAlexaff
Manoochehr Akhlaghinia, Farshid Torabi, Christine W. Chan

Bibliographic record

VenueSpecial Topics & Reviews in Porous Media An International Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsPetroleum Technology Research CentreUniversity of Regina
Fundersnot available
KeywordsRelative permeabilityPermeability (electromagnetism)ChemistryPetroleum engineeringThermalThermodynamicsMaterials sciencePorosityGeologyComposite material

Abstract

fetched live from OpenAlex

An experimental, coreflood setup is used and the Johnson–Bossler–Naumann technique is applied to measure relative permeability of a heavy oil (1174 cP at 28°C) and water in a 1.5 darcy consolidated sandstone core. In order to investigate the effect of temperature on the shape of heavy oil/water relative permeability curves, a series of coreflood tests is conducted at three different temperatures (28°C, 40°C, and 52°C). Experimental results show an increase of approximately 66% and 51% in water relative permeabilities when temperature increased from 28°C to 40°C and from 40°C to 52°C, respectively. In the case of oil relative permeabilities, a different effect is observed. The oil relative permeability curve increases by about 69% from 28°C to 40°C, but dramatically decreases about 32% when temperature increases from 40°C to 52°C. A hybrid lab-scale model representing the experimental setup is constructed using CMG-Builder and CMG-IMEX to simulate waterfloods at 40°C to 52°C. Comparing recovery curves with experimental data shows that simulation of thermal enhanced oil recovery processes such as hot waterflooding with relative permeabilities taken at the temperature of process gives a better recovery prediction compared to simulation with relative permeabilities taken at room temperature.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.412

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 communication0.0000.001
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 head0.019
GPT teacher head0.331
Teacher spread0.312 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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