Application of Organic Alkali for Heavy-Oil Enhanced Oil Recovery (EOR), in Comparison with Inorganic Alkali
Bibliographic record
Abstract
Alkali is an important component for alkali/surfactant/polymer technology for enhanced oil recovery (EOR). The mechanism and advantages of traditional inorganic alkali for EOR was reviewed in this paper. The rheological and dynamic properties of the combination of alkali and polymer were analyzed. The results show that the polymer solution with ethanolamine has better shear viscosity and elastic properties at room temperature. Surfactant (Alfaterra 123-8S-90) with concentration of 0.15 wt % was added into each alkali–polymer (AP) solution. No significant change was observed in rheological properties of AP solutions with and without surfactant. Emulsification tests show that ethanolamine has better performance with oil. Injectivity tests were also conducted. The results indicated that the residual resistance factor (RRF) for an ethanolamine–polymer solution is always higher at each flow rate tested, in comparison to a NaOH-based AP solution, which is beneficial for oil recovery. The interfacial tension (IFT) tests results indicated that ethanolamine has better synergy with the surfactant. Polymer adsorption using both static and dynamic measurements was conducted. Polymer solution in an ethanolamine system has lower adsorption for both measurements. The pressure comparison during core flooding experiments shows that it has higher injection pressure in ethanolamine conditions, which result in good sweep efficiency. The ethanolamine–polymer flooding showed a significant increase in oil recovery (15.33%) over NaOH–polymer flooding. After the addition of surfactant, the total recovery improves by 14.8% for ethanolamine–polymer–surfactant flooding over its inorganic counterpart. The better performance indicates that ethanolamine can become a potential alkali and can replace NaOH for EOR.
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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".