An Improved Numerical Simulator for Surfactant/Polymer Flooding
Bibliographic record
Abstract
Abstract Due to the high demand to increase oil production combined with the huge potential in enhancing oil recovery, surfactant/polymer (S/P) flooding is under increasing interest and importance in recent years. Numerous studies have shown that interactions between surfactant and polymer can be extremely important to the final displacement performance of S/P flooding, since the desired effect of polymer and surfactant may be enhanced or degraded as various slugs become mixed underground. Nevertheless, as far as we know none of the available commercial numerical simulators can account for the impact of these interactions. The study focuses on constructing an improved S/P numerical simulator. A series of experiments were performed on S/P mixed system and flooding process. The results show significant influence of S/P interactions on viscosity, interfacial tension, and adsorption, and the interactions can be totally different when injected into different S/P systems. Quantitative relationships of the interactions were then provided based on the results. Then, an S/P flooding mathematical model was established on the basis of mass conservation, with the description of various important phenomena during flooding process being included, especially for the interactions between surfactant and polymer. Adaptive implicit method was applied to solve the equations and a simulator was developed. The simulator was finally used to perform the numerical study of different S/P mixed systems, in which synergistic promotion, non-interaction and competitive repulsion were respectively presented. The displacement performance was the best when synergistic promotion existed between surfactant and polymer, followed by non-interaction, and competitive repulsion. In summary, a new method for the treatment of interactions between surfactant and polymer in numerical simulation was derived in this work. The improved simulator could enhance the matching degree between mathematical model and field data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".