Does injection sequence matter: A simulation study of chemical enhanced oil recovery processes
Bibliographic record
Abstract
In this study, CMG STARS was employed to simulate the experimental results conducted by Surtek in the lab, specifically focusing on a linear core flooding using alkaline, surfactant, polymer, and their combinations in different sequences to enhance oil recovery in the Warner Field.To begin with, experimental data, including essential core and reservoir information, oil and chemical properties, injection schemes, chemical adsorption data, interfacial tension (IFT) data, relative permeability data, and oil production data was extracted from the Surtek report.Subsequently, a comprehensive step-by-step procedure detailing the construction of the simulation model in CMG was described.This paper then presented the simulation data, followed by a comparison with the experimental data.The oil cut based on simulation results was plotted as well.It was found that there was improvement in oil cut when the chemicals were applied.It is noteworthy that, to achieve a better match with the experimental data, it was necessary to modify the relative permeability curve and chemical adsorption data.After matching the experimental data, sensitivity analysis was conducted by using the tuned simulation model.Specifically, the order of the ASP injection was modified by placing the ASP slug 3 with a lower polymer concentration ahead of ASP slug 1.A notable decrease in the oil production was found in the early stage but a higher final recovery factor was achieved in this injection strategy.This study reports a successful alignment between the simulation and experimental data.The sensitivity analysis using the predictions of the simulation model suggests the injection sequence is important in the chemical EOR processes.Depending on the priority of a high recovery rate or a high final recovery factor, a certain injection sequence may be selected for practical applications.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".