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Record W4253873007 · doi:10.2118/2009-049

Use of Biodiesel as an Additive in Thermal Recovery of Heavy-Oil and Bitumen

2009· article· en· W4253873007 on OpenAlexaffabout
Tayfun Babadagli, V. Er, Kamyar Naderi, Z. Burkus, B. Özüm

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAsphaltBiodieselEnvironmental sciencePetroleum engineeringWaste managementThermalProcess engineeringPulp and paper industryMaterials scienceChemistryEngineeringComposite materialOrganic chemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract Bitumen extraction in oil sands-ore water slurry systems was studied using lipids and lipid derivatives as surfactants to promote the efficiency of bitumen recovery. In this study canola oil, tall oil fatty acids (TOFA) which are by-products of pulp mills using the bleached Kraft process, canola oil fatty acids methyl esters which are known as biodiesel (BD), and canola oil fatty acids monoglycerides were used as surfactant additives. Experimental findings suggest that BD, i.e., fatty acids methyl esters, a blend of fatty acids methyl esters, and fatty acids monoglycerides could also be used as surfactant additives to increase the efficiency of bitumen recovery in thermal in-situ processes such as steam assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS) processes. Our experimental findings showed that the required dosage for the surfactant additives would be about 0.1 % of bitumen by mass. Also, interfacial tensions between bitumen and process water (?B,W) and BD and process water (ϒ B,W) were provided as supportive data for the applicability of the method proposed. This paper presents the initial observations. Experiments at high pressure conditions and the analysis of bitumen recovery and produced water chemistry are currently in progress. Background Oil sands deposits in northern Alberta, Canada contain about 142×109 cubic meter cube (m3) or 890×109 barrels of bitumen, which makes it one of the largest oil sands deposits in the world (AERCB, 1984). Four commercial plants are utilizing surface mineable oil sands deposits for bitumen production using oil-sands ore water slurry based extraction processes with total bitumen production capacity exceeding 106 barrels/day. Reduction of the surface and interfacial tensions play an important role in the efficiency of bitumen recovery in ore-water slurry systems (Moschopedis et al., 1977 and 1980; Speight and Moschopedis, 1977; Bowman, 1968; Baptista and Bowman, 1969), which are the basic reasons for the success of the Clark How Water Extraction (CHWE) process (Clark, 1939 and Clark and Pasternack, 1932). In the CHWE process, the solubility of naturally occurred asphaltic acids in bitumen which are partly aromatic, containing oxygen functional groups such as phenolic, carboxylic and sulphonic types are increased by the use of caustic NaOH, which act as surfactants reducing the surface and interfacial tensions. This process produces tailings with poor settling characteristics which result in the use of gypsum (CaSO4) as additive to alter its settling and consolidation properties. It is realized that the release water chemistry is harmed by the use of chemical additives in both extraction and tailings disposal processes (Allan, 2008 and 2008; Franklin et al. 2002). Novel extraction process aids, i.e. surfactant additives, have to replace the conventionally used additives, by which extraction efficiency would be improved without harming the fuel quality of bitumen, release water chemistry and geotechnical properties of the tailings. Our research was focused on the use of surfactants from external sources to eliminate the harmful effects of the additives used in the existing oil sands plants.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.993

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)0.0000.000

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 head0.023
GPT teacher head0.227
Teacher spread0.204 · 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 designObservational
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

Citations6
Published2009
Admission routes2
Has abstractyes

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