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Record W1967538559 · doi:10.2118/06-09-03

The Effect of Oil Sands Bitumen Extraction Conditions on Froth Treatment Performance

2006· article· en· W1967538559 on OpenAlexafffundabout
U.G. Romanova, M. Valinasab, E.N. Stasiuk, Harvey W. Yarranton, Laurier L. Schramm, W.E. Shelfantook

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2006
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsSyncrude (Canada)Saskatchewan Research Council (Canada)University of Calgary
FundersUniversity of WaterlooSyncrudeRussian Academy of SciencesKazan Federal UniversityUniversity of Calgary
KeywordsOil sandsAsphaltExtraction (chemistry)DilutionEnvironmental sciencePulp and paper industrySynthetic crudeWaste managementSteam-assisted gravity drainagePetroleum engineeringUnconventional oilGeologyChemistryMaterials scienceChromatographyFossil fuelEngineering

Abstract

fetched live from OpenAlex

Abstract Further development of oil sand deposits requires processing poorer quality oil sands while maximizing bitumen recovery, minimizing the water and solids content of the product bitumen, and minimizing overall energy consumption. Bitumen recovery requires two stages: extraction and froth treatment. This work focuses on the effect of process conditions in the Clark Hot Water Bitumen Extraction Process on froth treatment effectiveness. Laboratory approximations are used to represent the two commercialized froth treatment processes in Alberta:the "Syncrude Process," which is dilution with an aromatic solvent followed by centrifugation; and,the "Albian Process," which is dilution with a paraffinic solvent followed by gravity settling. Parameters considered are oil sand quality, extraction shear, extraction temperature, NaOH addition during extraction, froth treatment temperature, and froth treatment residence time. It was found that reduced extraction temperature results in lower bitumen recovery at least for low quality oil sands. Higher shear extraction may improve bitumen recovery, but decreases froth treatment effectiveness. For paraffinic solvent-based froth treatments, the addition of NaOH during extraction may be required to obtain optimum froth treatment of low quality oil sands. Introduction The Canadian oil industry is producing about 1 million barrels of bitumen and synthetic crude oil per day from oil sands and the production is expected to rise to 2 million barrels per day by 2012(1). Currently, both in situ and surface mining operations contribute almost equally to the total production. However, the production of synthetic crude from surface-mined oil sands is expected to take the lead in the next decade(2). Expansions of existing oil sand facilities are already underway and the addition of new facilities are planned within the next decade. There are two main stages to oil sand processing: extraction and froth treatment. The most common extraction process is hot water bitumen extraction. The oil sand is conditioned with hot water, either in a process vessel (conditioning drum) usually with NaOH added, or more recently in a pipeline (hydrotransport) usually with a smaller amount of NaOH added. During conditioning, the slurry is aerated and, ideally, the bitumen separates from the sand, and attaches to and spreads on the air bubbles. Water is added to the slurry, which is subsequently sent to a separation vessel. The bitumen- coated air bubbles are carried upwards to form a froth that is rich in bitumen. The froth also contains free water, emulsified water, and suspended solids(3, 4). The froth is collected in two stages yielding a primary and a secondary froth. For high-quality oil sands, a typical primary froth composition is approximately 66 wt% oil, 25 wt% water, and 9 wt% solids. A typical secondary froth has lower oil content (approximately 24 wt%) and higher water and solids contents (59 wt% and 17 wt%, respectively). Poorer quality oil sand froths have lower oil content and higher water and solids contents(5).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.924

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 head0.219
Teacher spread0.216 · 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

Citations44
Published2006
Admission routes3
Has abstractyes

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