MODEL-BASED ANALYSIS TO EVALUATE THE EFFECT OF POLYMER PROPERTIES AND PHYSICAL PHENOMENA ON POLYMER FLOODING OPERATIONS
Bibliographic record
Abstract
Polymer flooding, known for enhancing heavy oil displacement, may encounter efficiency-limiting physical phenomena. This work assesses the impact of key factors like viscosity, shear rate, and adsorption in field applications using a model-based approach on the EPIC001 case, a real offshore Brazilian heavy oil reservoir. In simulation results, increasing the displacing fluid viscosity using apparent or zero-shear functions, oil production and economic returns show improvements over waterflooding. The shear rate effect slightly increases oil production and enhances injectivity loss due to shear-thinning in polymer flow. However, modeling it increases computation costs, as it extends simulation run time from minutes to days, making it impractical for intensive processes like production optimization. An analysis of the method’s effectiveness shows that it varies based on the adsorption level considered. At its highest value, even with a higher oil recovery factor, the economic return was lower than using waterflooding. Combining shear rate and adsorption has a minimal impact on field indicators when compared to adsorption alone. This work enhances the comprehension of physical phenomena and non-Newtonian behavior in tertiary polymer flooding on heavy oil reservoirs and its impact on the production forecast. It also highlights important considerations for modeling-based procedures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".