Subsurface Upgrading of Heavy Oils via Solvent Deasphalting Using Asphaltene Precipitants. Preparative Separations and Mechanism of Asphaltene Precipitation Using Benzoyl Peroxide as Precipitant
Bibliographic record
Abstract
Subsurface upgrading of heavy oil via solvent deasphalting has been reported previously under laboratory and field conditions. However, these processes require a relatively high solvent-to-oil ratio (SvOR > 1:1 v/v) to induce subsurface asphaltene precipitation, increase oil production, and upgrade crude oil in situ. In our previous work, lab experiments demonstrated that asphaltene precipitants reduce the SvOR (∼30–50 vol %) for subsurface upgrading at initial reservoir conditions and when heat is also applied. In this work, the preparative separations were carried out using benzoyl peroxide (BP), Fe 2 O 3, and NiO nanoparticles as asphaltene precipitants for Venezuelan and Canadian heavy crude oils. Initial experiments showed that BP is the most effective additive, producing an increase of ∼21 wt % in the asphaltene content for a 2500 mg/kg dosage. Preparative separations at 5:1 vol/wt ratio and 50 °C showed that the order of activity as asphaltene precipitants is BP > NiO > Fe 2 O 3 . In the presence of nickel- and iron-containing precipitants, most of these metals are found in the asphaltenes indicating that the nanoparticles are acting as nucleation sites. Spectroscopic and mechanistic studies using BP as precipitant suggest a free radical mechanism that involves the thermally initiated homolytic cleavage of BP, follow by abstraction of a hydrogen atom from the asphaltenes or maltenes to produce free radical species. In the termination steps, the latter species react with each other to generate new asphaltene species that are not present in the original crude oils.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".