Investigating the Effect of Salinity on the Behavior of Asphaltene Precipitation in the Presence of Emulsified Water
Bibliographic record
Abstract
Asphaltenes precipitation and deposition is one of the main problems in the petroleum industries which has attracted the attention of many scholars. Precipitation and deposition of asphaltenes can lead to many problems in oil reservoirs such as plugging the pores of the reservoir rocks and changing the wettability of the rocks from water-wet to oil-wet. This ultimately causes a reduction or puts an end to production from reservoirs. Therefore, understanding the factors affecting the formation of asphaltenes precipitation can help us to avoid these drawbacks. Several factors including pressure, temperature, and composition changes have been studied in the literature. The effects of these parameters on the stability of asphaltenes are almost clear. However, the effects of water emulsions, which are formed during the water-based enhanced oil recovery (EOR) methods such as smart water and low salinity water flooding, on the instability of asphaltenes are still unknown and blurred. In this study, the effects of several synthetic brines which were prepared by different salts in a wide range of concentrations were investigated to understand the mechanism of ions on the instability of asphaltenes. It was found that the divalent cations have more effects on the instability of asphaltenes compared to monovalent cations due to the chelate formation. Furthermore, the presence of divalent anions in the system can hinder the effect of cations on the instability of asphaltenes.
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 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 | 0.001 |
| 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".