Evaluation of Polymers as Direct Thickeners for CO<sub>2</sub> Enhanced Oil Recovery
Bibliographic record
Abstract
In this paper, two commercial polymers, poly(vinyl ethyl ether) (PVEE) and poly(1-decene) (P-1-D), are tested to thicken CO 2 for CO 2 enhanced oil recovery (EOR). First, a series of laboratory tests are conducted to measure the cloud-point pressures of either polymer at different polymer solubilities in supercritical CO 2 and the equilibrium interfacial tensions (IFTs) of a light crude oil-pure or polymer-thickened CO 2 system under different reservoir conditions. Second, a capillary viscometer is used to measure the viscosities of polymer-thickened CO 2 at different test pressures. Third, a total of six high-pressure CO 2 coreflood tests are performed to examine the effects of polymer-thickened CO 2 on the total CO 2 EOR. It is found that at the same and low polymer solubility in pure CO 2, the measured cloud-point pressure of PVEE is much lower than that of P-1-D. The measured equilibrium IFT for polymer-thickened CO 2 at a high pressure is much lower than that for pure CO 2 . The PVEE- or P-1-D-thickened CO 2 viscosity is approximately (13 to 14) times higher than the pure CO 2 viscosity. The CO 2 coreflood test results show that PVEE- or P-1-D-thickened CO 2 flooding can further enhance oil recovery after a pure CO 2 breakthrough. The CO 2 breakthrough can be significantly delayed if polymer-thickened CO 2 is injected directly.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".