Simultaneous use of disulphide oil for chemical‐enhanced oil recovery by emulsion formation and stability with asphaltene deposition control
Bibliographic record
Abstract
Abstract Disulphide oil (DSO) is a by‐product of oil and gas refining processes that is generated during the removal of mercaptans and the sweetening of light hydrocarbons. Asphalt deposition, especially asphaltene deposition during enhanced oil recovery methods, reduces oil recovery from the reservoir, so the use of a substance such as DSO, which has the ability to control and reduce asphaltene deposition, can be effective in increasing oil recovery from the reservoir. In this research, a micromodel with a fracture design and a matrix that represents fracture reservoirs was utilized. These tests were conducted in two groups. The first group of tests is related to adding DSO to crude oil and using 70 to 30 vol.% oil–water emulsion containing salt, surfactant, and nanoparticles. The second group involved adding DSO to both crude oil and emulsion. The first group aimed at stimulation and the second group aimed at chemical enhanced oil recovery (C‐EOR). The formation and stability of water‐in‐oil emulsion was done by analyzing the average droplet size. As a result, in the first group of tests with the presence of DSO in the oil, by measuring the average diameter before and after injection of AOS surfactant, it was observed that the average droplet size decreased from 6.89 to 4.01 μm, which indicates an increase in the emulsion stability. In the second group, where DSO was present in both oil and water emulsion in injected oil, it can be seen that the average diameter of the droplets in the surfactant decreased from 5.12 to 3.21 μm.
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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) | 0.000 | 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".