Bibliographic record
Abstract
Abstract This paper addresses two questions for polymer flooding. First, what polymer solution viscosity should be injected? A base-case reservoir-engineering method is present for making that decision, which focuses on waterflood mobility ratios and the permeability contrast in the reservoir. However, some current field applications use injected polymer viscosities that deviate substantially from this methodology. At one end of the range, Canadian projects inject only 30-cp polymer solutions to displace 1000-3000-cp oil. Logic given to support this choice include (1) the mobility ratio in an unfavorable displacement is not as bad as indicated by the endpoint mobility ratio, (2) economics limit use of higher polymer concentrations, (3) some improvement in mobility ratio is better than a straight waterflood, (4) a belief that the polymer will provide a substantial residual resistance factor (permeability reduction), and (5) injectivity limits the allowable viscosity of the injected fluid. At the other end of the range, a project in Daqing, China, injected 150-300-cp polymer solutions to displace 10-cp oil. The primary reason given for this choice was a belief that high molecular weight viscoelastic HPAM polymers can reduce the residual oil saturation below that expected for a waterflood or for less viscous polymer floods. This paper will examine the validity of each of these beliefs. The second question is: when should polymer injection be stopped or reduced? For existing polymer floods, this question is particularly relevant in the current low oil-price environment. Should these projects be switched to water injection immediately? Should the polymer concentration be reduced or graded? Should the polymer concentration stay the same but reduce the injection rate? These questions are discussed.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".