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Record W1984677304 · doi:10.2118/137505-ms

The Effect of Low Molecular Weight Multifunctional Additives on Heavy Oil Viscosity

2010· article· en· W1984677304 on OpenAlexaff
Thomas B. P. Oldenburg, Harvey W. Yarranton, Steve Larter

Bibliographic record

VenueCanadian Unconventional Resources and International Petroleum Conference · 2010
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsViscosityAsphaltSolventDilutionChemical engineeringVolume (thermodynamics)ChemistryIn situMaterials sciencePetroleum engineeringAnilineThermodynamicsOrganic chemistryComposite materialGeology

Abstract

fetched live from OpenAlex

Abstract Highly viscous heavy oil and bitumen are becoming more important in the world energy mix as conventional resources decline. Any successful in situ recovery process must mobilize the highly viscous bitumen and move it to a production well. Standard methods use heat provided by steam (SAGD) or solvent (VAPEX), with encouraging combinations of steam and solvent (ES-SAGD) but these routes have inefficiencies. Alternative ways of mobilizing bitumen might involve viscosity reduction management of more fundamental controls on bitumen viscosity including managing internal physicochemical interactions between the interacting functional groups on different crude oil molecular components. Here we examine the impact of low molecular weight multiheteroatom species (LMWMH) as molecular Velcro linking high molecular weight moieties together by multiple interactions. Most species tested showed only a viscosity reducing dilution effect whereas THF/aniline as an additive showed an additional viscosity reducing effect particularly at low volume fraction of the additive. Considering that this additive is soluble in water and therefore easily transportable to the subsurface bitumen makes it a promising candidate for enhanced in situ bitumen viscosity reduction and thus improved in situ bitumen mobility.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.999

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)0.0020.000

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 head0.006
GPT teacher head0.220
Teacher spread0.214 · 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.

Study designNot applicable
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
Published2010
Admission routes1
Has abstractyes

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