Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".