Successful Story of Development and Optimization for Surfactant-Polymer Flooding in a Geologically Complex Reservoir
Bibliographic record
Abstract
Abstract For mature reservoirs, surfactant-polymer (SP) flooding is an attractive alternative to conventional waterflooding. However, it is a complicated process and the performance of SP flooding in complex reservoirs requires an accurate model that represents the reservoir features, chemical properties, and displacement mechanisms. This paper presents a successful application of miscible-tertiary SP flooding in an extremely heterogeneous reservoir. First, a series of numerical simulations in both homogeneous and heterogeneous cases were investigated and analyzed by a CMG™ simulator. Then a mathematical model was developed based on the Langmuir isotherm theory in order to fully integrate adsorption phenomenon into a reservoir model for controlling and reducing this effect during the SP flooding process. Small of polymer/surfactant adsorption leads to a small amount of chemical required for injecting and decreases operational cost. Based on the above achievements, SP flooding was successfully applied for White Tiger - the biggest offshore oil field with high heterogeneity and complex geological characteristics in the Viet Nam continental shelf. An optimal range of operated conditions that include polymer solution properties, injection pressure and injection rates are proposed with the objective of optimizing the SP process in the White Tiger field. The simulation results show that SP flooding is the best recovery schemes in comparison with waterflooding, pure polymer flooding and pure surfactant flooding. A significant increase in oil production has been achieved by the effect of surfactant and polymer which is a really successful evidence of SP flooding in complex reservoirs.
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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) | 0.000 | 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 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".