Successful Story of Development and Optimization for Surfactant-Polymer Flooding in a Geologically Complex Reservoir
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it