Bibliographic record
Abstract
Abstract One of the main uncertainties when designing polymer floods is the polymer injectivity, an important parameter that can affect the economics of the process. Reservoir simulation can be used to forecast injectivity, but the process is not straightforward and can be affected by grid size and other factors. Analytical methods are also available for that purpose, but they are considered too simplistic to deal with realistic reservoir conditions. The aim of this paper is to show that this is not the case and that simple analytical tools can be accurate and of great help to predict or history match polymer injectivity. The analytical method has been developed by Lake in his classical textbook on Enhanced Oil Recovery, but few applications are documented in the literature. This paper will review the method and corresponding equations before presenting several actual field cases of injectivity in polymer flood pilots or tests from several countries that have been matched analytically. Although it has not been used very often, the method has been found to give very good results in most of the field cases tested in a variety of situations; these cases will be presented along with recommendations on how to apply the method and a discussion of the results. Sensitivities to the various parameters will also be presented. Once the equations are programmed in a spreadsheet, the matching process takes only a few minutes and it is easy to run various scenarios and sensitivities. Polymer injectivity remains one of the less understood and less predictable aspects of polymer flood projects. This paper will encourage engineers who are planning such projects to use simple yet accurate analytical tools before embarking in more complex and time-consuming reservoir simulations.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".