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Gas Generation during Electrical Heating of Oil Sands

2016· article· en· W2512619717 on OpenAlexaff
Hassan Hassanzadeh, Thomas G. Harding, R.G. Moore, S. A. Mehta, Matthew Ursenbach

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsNexen (Canada)University of Calgary
Fundersnot available
KeywordsCrackingCokeAsphaltOil sandsThermalVolume (thermodynamics)Gas compositionChemistryHydrogenHydrogen sulfidePetroleum engineeringMineralogyMaterials scienceThermodynamicsSulfurComposite materialGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

Electrical heating of oil sand formations involves high temperatures, which can lead to in-situ gas generation as a result of aquathermolysis and thermal cracking of bitumen. The thermal cracking of bitumen can also lead to formation of coke at higher temperatures. Prediction of the volume of the produced gas and coke is very important in design and implementation of thermal processes dealing with high temperatures such as electrical heating and steam assisted gravity drainage with addition of oxygen. In this work a reaction kinetics model has been developed based on the experimental data of in-situ gas generation from an Athabasca bitumen sample. The kinetic parameters were estimated using the experimental data in the temperature range 360–420 °C. The results show that coke formation can be significant at higher temperatures. An important observation was that a plateau to H 2 S production is not expected at higher temperatures. In addition, the results show that operation at 370 °C produces a gaseous composition that minimizes the volume of the produced gas at the surface. A simple scaling analysis is presented that allows clarification of the scatter that has been observed in the reported produced hydrogen sulfide versus the operation temperature. The developed model provides a useful tool for the estimation of produced gas composition during thermal recovery processes.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.362

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 head0.219
Teacher spread0.210 · 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

Citations27
Published2016
Admission routes1
Has abstractyes

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