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Record W2059779354 · doi:10.2118/97913-pa

Heavy Oil and Bitumen Dehydration—A Comparison Between Disc-Stack Centrifuges and Conventional Separation Technology

2007· article· en· W2059779354 on OpenAlexfundno aff
Johan Agrell, Mark Faucher

Bibliographic record

VenueSPE Production & Operations · 2007
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersSyncrude
KeywordsCentrifugeAsphaltStack (abstract data type)Petroleum engineeringAsphalteneDehydrationSteam-assisted gravity drainageEnvironmental scienceLight crude oilViscosityPetroleumMaterials scienceWaste managementChemistryOil sandsChemical engineeringGeologyEngineeringComputer science

Abstract

fetched live from OpenAlex

Summary Recent technological advances are making the exploitation of heavy crude oil reserves increasingly profitable. This paper compares nozzle-type disc-stack centrifuges to conventional separation technology for dehydration of heavy oil and bitumen. The nature and composition of heavy oil leads to a number of undesirable properties, such as its tendency to form stable emulsions in the presence of asphaltenes, particles, and other emulsifiers occurring naturally in the oil. This, combined with a high viscosity and a relatively high solids content, makes dehydration a challenging task that introduces new concerns when compared to dehydrating light crude oil. As the density of the heavy oil increases and approaches that of water, conventional static and gravity-based separation systems become unacceptably large and require excessive heating and chemical addition to produce pipeline-specification oil. Hence, the disc-stack centrifuge is proposed as a compact and efficient solution, enabling breakdown of stable emulsions and removal of dispersed water droplets and solid contaminants from heavy and viscous crudes in both onshore and offshore installations.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.631

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 communication0.0000.001
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 head0.013
GPT teacher head0.293
Teacher spread0.280 · 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

Citations6
Published2007
Admission routes1
Has abstractyes

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