Optimizing nanofluid additives for enhanced thermophysical properties in anionic crude oil for <scp>EOR</scp> applications
Bibliographic record
Abstract
Abstract This research delved into the effects of introducing multi‐walled carbon nanotubes (MWCNTs) and sodium dodecyl sulphate (SDS) into crude oil with an anionic base, aiming to enhance its thermophysical attributes in the EOR process. The investigation encompassed a range of properties, including interfacial tension, viscosity, density, and electrical conductivity, measured using an LV rheometer with spindle 18. Various ratios of MWCNTs and SDS were mixed with the crude oil to evaluate their impacts. The study uncovered that the 1:1 ratio of MWCNTs to SDS achieved a noteworthy 10% reduction in interfacial tension while minimally affecting viscosity, showing promise for practical applications. Temperature had diverse effects on viscosity and interfacial tension, displaying fluctuating trends. The highest observed result was a substantial 190% viscosity increase at 40°C with a 2:1 ratio, whereas the lowest was a slight −0.81% density change at 60°C for the 1:1 ratio. Additionally, interfacial tension consistently decreased over time for all ratios at various temperatures, with the most significant reductions observed at 40 and 60°C, particularly for the 1:2 and 1:1 ratios. Furthermore, adding MWCNTs and SDS led to a remarkable drop in electrical conductivity, indicating a substantial decrease in salt content within the crude oil. However, it was noted that higher MWCNT concentrations might not be cost‐effective, emphasizing the importance of optimizing additive ratios for a balance between performance and economic viability. This study highlights the potential of tailored nanofluid formulations to improve the thermophysical properties of crude oil, potentially enhancing extraction and refining processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".