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Record W1981452692 · doi:10.2118/03-08-05

Temperature Effects From the Conditioning and Flotation of Bitumen From Oil Sands in Terms of Oil Recovery and Physical Properties

2003· article· en· W1981452692 on OpenAlexafffund
Laurier L. Schramm, E.N. Stasiuk, Harvey W. Yarranton, Brij Maini, Bill Shelfantook

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2003
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSyncrude (Canada)Saskatchewan Research Council (Canada)University of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOil sandsAsphaltSurface tensionPhysical propertyViscosityPetroleum engineeringEnhanced oil recoveryGeologyConditioningEnvironmental scienceMaterials scienceComposite materialThermodynamics

Abstract

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Abstract Batch extraction tests show that, for Athabasca oil sands, the water-based conditioning/flotation process can be adjusted from 80 to 50 ° C conditions without substantial changes in optimal process aid addition level or primary oil recovery obtained. When the process temperature is further reduced to 25 ° C however, an order of magnitude reduction in primary oil recovery is obtained, suggesting that one or more key process variables have undergone a substantial change. Our studies with process additives suggest that several key physical properties undergo major changes, including bitumen viscosity, interfacial tension, and interfacial charge. If these are addressed, then comparable optimum primary oil recoveries can be achieved under all of 25, 50, or 80 ° C conditions. This is a significant result in terms of identifying the key mechanism(s) by which good primary froth recovery can be achieved. It is shown that the interfacial property changes, in particular, are consistent with the expected thermodynamic conditions necessary for efficient bitumen separation and flotation. Introduction Oil sands are unconsolidated sandstone deposits containing bitumen, which is chemically similar to conventional crude oil, but has a greater density (a lower API gravity) and a much greater viscosity. Because sediments were brought in to the Athabasca deposit area from different sources and at different times, the oil sands occur as a mixture of sediment types, overlain by varying thicknesses of non-oil bearing formations(1, 2), so that a diverse number of distinct depositions can be discerned(2–5). Accordingly, the oil bearing sands have great variability in their compositions and properties and while in oil sand processing the general principles of mineral flotation apply, oil sand composition and structure, and their variations, have a great impact on the way the flotation must be operated. The hot water flotation process for oil sands is a separation process in which the objective is to separate bitumen from mineral particles by exploiting the differences in their surface properties. The slurry conditioning process involves many process elements, including ablation, mixing, mass and heat transfer, and chemical reactions leading to the separating of bitumen from the sand and mineral particles. Adopting the water-wet model for Athabasca oil sand, one assumes that a thin aqueous film already separates the bitumen from the sand; this separation needs to be enhanced. Disengagement of bitumen from solids will thus be favoured if their respective surfaces can be made more hydrophilic, since a lowering of surface free energy will accompany the separation. The phase separation is enhanced by the effects of mechanical shear and disjoining pressure. Although there are many variables, including water addition ratios, mechanical energy input levels, chemical addition levels, temperatures, and residence times, process efficiency is more sensitive to some variables than to others(6, 7). Early studies led to the identification of base (NaOH) addition level as the preferred process variable [see the review in Reference (8)] and it was shown by Sanford(9) that NaOH addition level could be controlled in response to fines level in the feed.

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 score0.034
Threshold uncertainty score0.960

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 head0.003
GPT teacher head0.171
Teacher spread0.168 · 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

Citations47
Published2003
Admission routes2
Has abstractyes

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