Emulsion Characterization of the Heavy Oil-Alkaline Water System in Alkaline Flooding Mechanism Investigation Using a Combination of Modified Bottle Test and Sandpack Flooding
Bibliographic record
Abstract
Due to the diversity of alkali categories and reservoir conditions, the varied oil recovery driving mechanism of alkaline flooding is subjected to different types of emulsion generation. In this study, a modified bottle test method that assesses major emulsion type formation for preliminary prediction of alkaline flooding performance in oil recovery is introduced. The modified method considers the necessary energy input required for mixing immiscible bulk phases at low interfacial tension (IFT) regions to improve the representativity of emulsion formation in the bottle test to that of in porous media. To accurately evaluate the emulsion type and phase volume distribution from the bottle test, each emulsion phase after aging in the test bottle was sampled and its water content was measured through Karl Fischer titration. Afterward, material balance calculations other than pure volume observation were applied to quantify the emulsion volume and determine the major emulsion type formation. It is found that the majority of emulsion effluent type from the sandpack flooding test were in agreement with the bottle test forecast which proved the feasibility of the modified bottle test method. The statistically optimized experimental designs were implemented due to the simplicity of the new bottle test method and it considerably cut the time expense regarding the alkaline flooding performance prediction. The high versatility of the modified bottle test ensures that the alkali usage is not limited to the inorganic alkalis mentioned in this study; other type of alkaline solutions can also be used for further expanding the scope of its application.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".