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Record W4243067925 · doi:10.2118/2003-211

The Potential For Carbon Dioxide Sequestration in Oil Sands Processing Streams

2003· article· en· W4243067925 on OpenAlexaff
R.J. Mikula, O. Omotoso

Bibliographic record

VenueCanadian International Petroleum Conference · 2003
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsSTREAMSCarbon dioxideOil sandsCarbon sequestrationEnvironmental sciencePetroleum engineeringEnhanced oil recoveryGeologyComputer scienceChemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract The CT or consolidated tailings process involves chemical amendments to combine the clays and fines in oil sands mature fine tailings or thickened tailings with the coarser sand components to create a nonsegregating tailings (NST) mixture that will rapidly consolidate. Over the years, several amendment chemicals have proven to be useful in controlling the fluid tailings properties so that they may support a sand loading and remain nonsegregating. Suncor has several years of commercial scale operating experience with gypsum as the CT process aid and in the years leading up to the commercialization of the CT process at Suncor, carbon dioxide was also investigated as a CT process aid. With the concerns over carbon dioxide related to the Kyoto protocol, the extent to which carbon dioxide is trapped and chemically sequestered in the CT process has been investigated. The mechanism by which carbon dioxide addition affects the strength of the mature fine tailings or fluid tailings component has been investigated, and the potential for carbon dioxide sequestration has been quantified. Depending upon the availability of gypsum as a CT or NST additive, carbon dioxide could be a useful alternative. Introduction Creation of a suitable CT mixture involves creation of a nonsegregating mixture of sand, clay, and water; rapid initial settling (water release) of the mixture; and ultimate consolidation of the mixture. Extensive studies by Scott et al.1 have demonstrated that there is a wide range of sand-to-fines ratios, solids contents, and gypsum addition levels where these criteria are met. Poor control of calcium levels in the release water and the scaling problems associated with high calcium and bicarbonate concentrations have been major problems associated with gypsum CT. To circumvent these problems, polymeric flocculants and aluminum salts (alum) have been investigated at Syncrude Research2 and CANMET3 as substitutes for gypsum, with alum showing some promise. In these studies, the dewatering rate and segregation index of alum CT were shown to be similar to those for gypsum CT. Calcium ion concentrations in the release water of alum CT are significantly lower than in the release water of gypsum CT. However, there is a decrease in the release water alkalinity (bicarbonate) with alum - CT, which can potentially increase the caustic demand in extraction where bicarbonate alkalinity has been shown to improve the conditioning and flotation process. The bicarbonate ion disperses the clays, improving oil sand conditioning and flotation. The potential for using CO2 as a substitute for gypsum was evident from high viscosities observed in CO2- saturated MFT4. Under normal circumstances, high MFT viscosity is essential for good CT production. In fact, one would need about 3000 ppm of gypsum in the MFT to match the viscosity produced by CO2-saturated MFT. In the highly buffered CT mixture, pH reduction associated with CO2 addition quickly reverts to the initial pH of the MFT. At low clay-to-water ratios, (C:W), the nonsegregating property of a CT mixture is controlled by the extent to which clay flocs can support sand grains. Divalent cations achieve this strength through double-layer compression, which promotes coagulation of the clays3.

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 score0.631
Threshold uncertainty score0.872

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 head0.009
GPT teacher head0.225
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 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

Citations1
Published2003
Admission routes1
Has abstractyes

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