Experimental Study of Solvent-Based Emulsion Injection to Enhance Heavy Oil Recovery in Alaska North Slope Area
Bibliographic record
Abstract
Abstract This paper is the report for the second stage research of nano-particle and surfactant-stabilized solvent-based emulsion experimental study for the heavy oil in Alaska North Slope Area. The core flooding studies under laboratory conditions were implemented after the bench tests, which is including the phase behavior test, rheology studies and interfacial tension measurement. And these studies provide the optimum selecting method for the nano-emulsion which could be used in the core flooding. The experiment results suggest this kind of emulsion flooding is a good optional EOR (enhanced oil recovery) process for heavy oil reservoirs in Alaska, Canada after primary production, where heavy oil lacks mobility under reservoir conditions and is not suitable for the application of the thermal recovery method because of environmental issues or technical problems. Core flooding experiments were performed on cores with varied permeabilities. Comparisons between direct injection of nanoemulsion systems and nano-emulsion injections after water flooding were conducted. Oil recovery information is obtained by material balance calculation. In this study, we try to combine the advantages of solvent, surfactant, and nano-particles together. As we know, pure miscible solvent used as an injection fluid in developing the heavy oil reservoir does have the desirable recovery feature, however it is not economical. The idea of nano-particle application in an EOR area has been recently raised by researchers who are interested in its feature-reaction catalysis-which could reduce in situ oil viscosity and generate emulsion without surfactant. Also, the nano-particle stabilized emulsions can long-distance drive oil in the reservoir, since the nano-particle size is 2-4 times smaller than the pore throat. In conclusion, the nano-emulsion flooding can be an effective enhancement for an oil recovery method for a heavy oil reservoir which is technically sensitive to the thermal recovery method.
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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 | 0.001 | 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".