Effect of Emulsified Water on Asphaltene Instability in Crude Oils
Bibliographic record
Abstract
Understanding asphaltene precipitation and subsequent deposition during oil production is of great importance for the oil industry nowadays because of the potential risk associated with this heavy fraction in plugging wellbores and production equipment. Although water is commonly present in the produced fluids, because of instrument limitations and inadequate techniques, it is usually separated from the oil prior to any experimental analysis. Therefore, the effect of water on asphaltene stability and deposition tendency is not completely understood, and the information available in the literature is scarce. In this work, the effect of emulsified water on asphaltene instability in crude oil systems is investigated. Three crude oils and one bitumen sample were used in this study. The crude oils had American Petroleum Institute (API) gravities ranging from 26° to 40° and asphaltene content between 1.2 and 13 wt %. Model oils were also prepared with asphaltenes extracted from these crude oils. A total of nine systems were investigated with and without the presence of emulsified water. It was found that, for the crude oils from the Middle East and Canada and their corresponding model oils, the addition of water did not have a significant effect on either the detection of asphaltene precipitation or the amount of precipitated asphaltenes. However, the stability of asphaltenes in the crude oil from the Gulf of Mexico and the model oils from the Athabasca bitumen (containing n -C 5 and n -C 7 asphaltenes) was significantly affected by the presence of water. The experimental evidence suggests that some asphaltenes are more prone to interact with water at the oil–water interface. This work provides a simple technique to screen whether water has an effect on asphaltene stability for a given crude oil at ambient pressure and different temperatures. With this study, we aim to contribute to a better understanding of the interaction of water and asphaltenes in crude oil systems, which will eventually lead to the development of cost-effective strategies for the mitigation of this flow assurance problem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".