A Model-Based Production Strategy Selection Considering Polymer Flooding in Heavy Oil Field Development
Bibliographic record
Abstract
Abstract Polymer flooding is a chemical EOR technique in which polymer is added to injection water, increasing its viscosity, decreasing water-oil mobility ratio and hence improving sweep efficiency. This recovery method create unique conditions that are absent in traditional water flooding, which makes an adequate production strategy essential to the success of the project. This work is part of a complete decision analysis process with polymer flooding and the objective here is to present a methodology for production strategy selection considering water and polymer flooding as recovery mechanism options in heavy oil reservoir, guiding the decision maker to have an accurate tool to compare water and polymer flooding strategies and decide which one is the best option in determined project, using numerical simulation and economic analysis. The methodology is divided in seven steps based on variable hierarchy. The optimization process aims the maximization of NPV and the variables optimized are: number and location of wells, production systems capabilities, schedule of well drilling, production and injection rates, economic water cut limit for well shutdown, polymer concentration and slug size. The application of the methodology is made in a model that represents an offshore heavy oil Brazilian field. For comparison purposes, the methodology is also applied considering water flooding as recovery mechanism. The results show the feasibility in applying polymer flooding in early heavy oil field development with better economic return than water flooding. Moreover, this work shows the importance of applying the process separately for water and polymer flooding, otherwise wrong decisions can be made if simple comparisons are performed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".