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Record W2553025336

Viscosity Reduction in Extra Heavy Crude Oils

2005· article· en· W2553025336 on OpenAlexaboutno aff
Mark W. Badger, Harold H. Schobert

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCrude oilEnvironmental scienceSynthetic crudeViscosityDistillationPetroleumPetroleum engineeringPulp and paper industryWaste managementFossil fuelChemistryUnconventional oilGeologyMaterials scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Latin American sources are expected to increase production to around 4.2 million bbl/day by 2005. Of this 1 million bbVday is expected to be extra-heavy Orinoco oil from Venezuela. It has been projected that imports to the US from Latin America will increase by around 2.7 million bbVday during the same period. As well as this, a big increase in crude oil production will come heavy crude oil reserves in Western Canada. These reserves are intended to be produced a diluted heavy crudes and as synthetic oils, with vaqing degrees of upgrading. It is expected that by 2005 another 700,OO bbVday will be coming from Canadian heavy oil fields. Most of this will end up in the US Mid-West Market. This supply will replace the US supplies at lighter crude. With such increases of heavy crude oil sources making there way into the US market in the near future now seems a good time to exploit new avenues of research into the reduction at their viscosity. Some of these crude oils can have viscosities in excess of 15,000 centistokes at 100°F (2). In order for these crude oils to be transported via pipeline from the source, the viscosity must be lower than 150 centistokes at 100°F. The crude oils being very viscous on extraction have to go through some processing e.g. gas plasticization, thermal cracking or blending with lighter distillate fractions, to enable them to be transported by pipeline. Taking blending for an example, typically the crude oils are blended with a kerosene distillate fraction. This process has its disadvantages. In some cases up to 30 wt.% of kerosene must be added to sufficiently reduce the viscosity, this uses up a great quantity of a valuable commercial product. Also the added kerosene must be processed again through the refinery along with heavy crude oil. It is widely assumed that the asphaltene molecules in oils agglomerate to form micelle-like clusters. Interactions between these clusters contribute towards the viscosity of the oils. By breaking these agglomerates apart viscosity will be reduced. The kerosene used to cut the oils and reduce viscosity only does so by being an effective diluent. It does not break down the agglomerates by any significant amount. The following work is from a scoping study, the objective of which was to see whether the viscosity of extra heavy crude oils could be reduced by the addition of other chemical compounds and/or distillate fractions, which may break down the asphaltene agglomerates, and thus achieve greater viscosity reductions, when compared with kerosene. The intention is to then augment the better of these additive compounds with kerosene, thus initiating viscosity reduction for less volume of diluent. In this period of initial investigation several dispersants with differing physical and chemical properties have been assessed for their effect on viscosity reduction. Figure 1 shows the chemical structure and the polarity of the compounds, in Debye units (DU). The compounds were chosen for their ring type structures. The presence of x electrons in the ring may play a role in the interaction between the compounds added and the n electrons in the polyaromatic systems of the asphaltene agglomerates. The compounds were kept to one ring to keep the size of the molecules small and allow for a greater diffusion through the crude oil matrix, and penetration into the asphaltene agglomerates.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2005
Admission routes1
Has abstractyes

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