Decreasing Asphaltene Precipitation and Deposition during Immiscible Gas Injection Via the Introduction of a CO2-Soluble Asphaltene Inhibitor
Bibliographic record
Abstract
Summary In this study, the ability of dilute concentrations of toluene to act as a CO2-soluble asphaltene stabilization agent capable of inhibiting asphaltene precipitation during immiscible CO2 injection was assessed. Phase behavior results indicated that 1,000 to 20,000 ppm toluene could readily dissolve in CO2 at cloudpoint pressures that are well below the formation pressure and typical CO2 minimum miscibility pressure (MMP) values during gas-based enhanced oil recovery (EOR). Single-phase solutions of the modified gas (CO2/toluene) were then combined with asphaltenic oils in oil swelling phase behavior tests to demonstrate that the presence of toluene increased the amount of CO2 that dissolved into reservoir crude oil at a specified temperature and pressure. However, asphaltene precipitation diminished, apparently because the effect of the increased asphaltene solvent strength of toluene was more significant than the increased amount of CO2 (an asphaltene antisolvent) that entered the oil-rich phase. During the injection of CO2/toluene solution into cores initially saturated with crude oil and brine, compared to the injection of pure CO2, asphaltene deposition declined during the injection of CO2/toluene mixtures for asphaltenic volatile and intermediate oils from 3.7 wt% to 0.7 wt% and 5.9 wt% to 1.7 wt%, respectively. Based on the asphaltene particle-size analysis, the CO2/toluene mixtures can stabilize oil particles and simultaneously reduce asphaltene aggregation more effectively than pure CO2.
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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 | 0.000 |
| 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".