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Record W2073847407 · doi:10.2118/2009-081

Use of Calcium Sulfate to Accelerate Densification while Reducing Greenhouse Gas Emissions from Oil Sands Tailings Ponds

2009· article· en· W2073847407 on OpenAlexafffund
Sylvain Bordenave, Elizabeth Ramos, Shiping Lin, Gerrit Voordouw, Lisa M. Gieg, Chengmai Guo, Sean Wells

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsSuncor Energy (Canada)University of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCitationTailingsGreenhouse gasLibrary sciencePetroleumMining engineeringArchaeologyEnvironmental scienceEngineeringGeologyComputer scienceGeographyChemistry

Abstract

fetched live from OpenAlex

Abstract Tailings ponds contain large volumes of fine tailings that often settle slowly on a timescale of years. Accelerated densification can be achieved by addition of agents such as calcium sulfate (gypsum), although some microbial activity such as methanogenesis may also accelerate densification. The latter process, however, contributes to undesired methane emissions from tailings ponds. It is known that the presence of sulfate can curtail the activity of methane-forming bacteria by diverting microbial activity from the production of methane to the reduction of sulfate to sulfide. We sought to characterize the microbial processes in a tailings pond managed by Suncor in order to estimate their contribution to densification and reduction of greenhouse gas emissions. The sulfate and sulfide concentrations, sulfide oxidation potential, and rates of microbial sulfate reduction and methanogenesis were measured as a function of depth in a Suncor tailings pond. This pond is routinely treated with gypsum (CaSO4-2H2O) to consolidate fine tailings. The highest concentration of sulfate was found at the pond surface (over 6 mM or ∼ 575 ppm) where no sulfide was detected. At various depths, the sulfate and dissolved sulfide concentrations measured correlated well with each other and with the rates of sulfate reduction. In general, the highest rates of methanogenesis (up to 80 mmol CH4/m3 tailings/d) were measured where the sulfate reduction rates were lowest and vice versa, which supports the notion that methanogenesis will occur when sulfate-reducing activity is low. The fluctuating rates of sulfate reduction and methanogenesis we measured as a function of depth also shows that different microbial activities occur at discreet levels within the pond, probably due to the availability of sulfate. Based on the average rate of sulfate reduction measured in the pond samples, 10 mmol sulfate reduced/m3 tailings/d, the consumption of pond hydrocarbons by sulfate-reducing bacteria could potentially inhibit the formation of over 2 million L of methane per day from a typical pond harboring ∼107 m3 of tailings. Although the stimulation of sulfate reduction would produce undesirable H2S, our data suggest that sulfide is converted to sulfate at the pond surface due to either chemical or microbial oxidation. The data collected so far support a model in which sulfide, formed by reduction of sulfate at depth, is carried upwards in gas bubbles (possibly methane) and is then oxidized back to sulfate in the upper oxygenated layers of the pond. Our observations show that the use of calcium sulfate as a consolidation agent has the additional advantage of reducing greenhouse gas emissions from tailings ponds. Introduction Tailings ponds minimally contain water, sand, clays, and residual bitumen and hydrocarbon diluent. A typical pond has a large volume (∼107 m3) of a water-clay-bitumen suspension as its core surrounded on all sides by sand, which has segregated during oil extraction. The suspension, referred to as fine tails, settles slowly on a time scale of years, compromising water reuse and eventual pond reclamation.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.436
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.269
Teacher spread0.219 · 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.

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

Citations5
Published2009
Admission routes2
Has abstractyes

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