Investigation of the Effect of a Nanofluid Injection on Heavy Oil Recovery from Carbonate Reservoirs
Bibliographic record
Abstract
Heavy oil resources in fractured carbonate reservoirs are massive. Heavy oil carbonate reservoirs account for at least 26% of the original bitumen-in-place in Canada and for 25 – 30% of the total oil-in-place in the Middle East region. However, the average ultimate recovery factor remains low (<20%). Unfavorable conditions such as an adverse viscosity ratio between heavy oil and water and an oil-wetting preference of a carbonate rock are mainly responsible for low recovery factors. This thesis examines the use of nanofluids for heavy oil recovery from carbonate reservoirs. The nanofluid investigated in this study consisted of a dual-surfactant system and hydrophilic silica nanoparticles in a brine solution. The dual-surfactant system was tailored for carbonate reservoirs and consisted of cationic and non-ionic surfactants in diluted concentrations (≤0.2 wt. %). It was found that the nanofluid was able to shift an oil-wet carbonate surface to intermediate-wet conditions and stabilize an emulsion of heavy oil and water. The effect of a nanofluid injection was studied in core flood experiments. It was found that the nanofluid injection was able to intensify heavy oil production and performed better than a waterflood. It was suggested that the increase in heavy oil production was achieved by the means of entrainment of oil-in-water and water-in-oil emulsions and emulsion plugging.
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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".