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Record W4233612787 · doi:10.2118/2006-131

Laboratory and Pilot Experience in the Development of a Conventional Water Based Extraction Process for the Utah Asphalt Ridge Tar Sands

2006· article· en· W4233612787 on OpenAlexaffabout
R.J. Mikula, V.A. Munoz, O. Omotoso

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsOil sandsRidgeAsphalttar (computing)Extraction (chemistry)Process (computing)GeologyEnvironmental sciencePetroleum engineeringGeotechnical engineeringComputer scienceMaterials science

Abstract

fetched live from OpenAlex

Abstract The belief that unconventional tar sands deposits are oil-wet has led to a focus on solvent-based bitumen extraction processes for these types of materials. Utah's Asphalt Ridge deposit is a case in point, except that, under certain conditions, this ore is in fact amenable to a conventional water-based extraction process. The thermal, mechanical, and chemical environments necessary to make the Asphalt Ridge ore behave like an Alberta Athabasca oil sand are outlined, along with the typical criteria which must be satisfied for a novel extraction process to be viable. Laboratory-scale demonstrations of the efficacy of a Clark-style hot water extraction process for the Asphalt Ridge tar sands were subsequently confirmed on a twenty ton per hour pilot scale. In addition, the scarcity of water at the mining and extraction operation in Utah led to the development of an aggressive tailings treatment process, which also offers lessons for tailings handling in the surface mining of oil sands in Alberta. Introduction CANMET's Energy Technology Centre in Devon became involved in the Asphalt Ridge tar sands project when it was a solvent-based extraction operation hampered by a significant emulsion build up in the recycle water. In working to develop a solution to the emulsion buildup, it became apparent that, using the solvent based extraction process, solvent losses associated with solvent and clay mineral interactions would be unacceptably high. As a result, a series of standard tests were applied to Asphalt Ridge tar sand samples in order to assess the potential of a water-based extraction process(1,2). Surprisingly, some of these nominally "oil wet" tar sands performed very well, indicating that the Asphalt Ridge tar sand bitumen could be extracted using commercially proven technology developed over the last 30 years in Alberta(3–8). In order to achieve bitumen recoveries similar to those for Athabasca oil sands, significantly higher mechanical energy levels were required, along with high temperatures. Since the early 1990s, the operating temperature used in commercial processing of Athabasca oil sands has been from about 80 °C to less than 50 °C by increasing the mechanical energy input(9–12). By maintaining both mechanical and thermal energy inputs at high levels, the difficult-to-process Asphalt Ridge tar sand showed bitumen recoveries of approximately 90%, similar to the Athabasca commercial operations. The Asphalt Ridge tar sand samples that did not perform well in bench-top laboratory assessments were found to be weathered or oxidized, conditions that also inhibit extractability in the Athabasca oil sands in Alberta(13–17). The difficulties encountered by earlier researchers in processing the Asphalt Ridge tar sand without solvents may have been due to improper handling of cores or bulk samples resulting in oxidation or weathering(18–21). In spite of the nominally oil-wet nature of the Asphalt Ridge tar sand, the bitumen can be extracted using a water-based process. Water-based bitumen extraction involves two key steps, referred to as conditioning (where the bitumen is separated from the mineral) and flotation (where the bitumen is concentrated in a series of flotation cells).

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.956

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 head0.027
GPT teacher head0.256
Teacher spread0.230 · 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 designSimulation or modeling
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

Citations2
Published2006
Admission routes2
Has abstractyes

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