Surfactant synergy on rheological properties, injectivity, and enhanced oil recovery of viscoelastic polymers
Bibliographic record
Abstract
Surfactants synergized viscoelastic polymers can effectively balance the thickening and injectivity ability of the composite system and improve its enhanced oil recovery (EOR) effect. This work systematically studies the impact of concentration, compounding methods with surfactants, surfactant types, and salt concentrations on the rheological behavior of modified carboxymethyl cellulose (mCMC) based on the shear rheological properties. Then, injectivity experiments of the above solutions were carried out to compare the impact of differences in rheological properties on solution injection performance and optimize the injection parameters. Finally, oil displacement experiments were conducted to verify the mCMC viscoelasticity on the EOR effect. Experimental results show that surfactants can weaken the effect of shear on changing solution viscosity, and zwitterionic surfactants have the most obvious effect. The viscoelasticity of mCMC solution causes it to exhibit extensional viscosity, which gradually dominates as the shear rate increases, resulting in poor injection performance. Therefore, as the injection velocity increases, the injection factor has a maximum value (corresponding to the optimal injection velocity, about 10 ft/D). After that, increasing the injection velocity will greatly reduce mCMC injectivity under a higher extensional viscosity. When the shear rheology curves are similar and the injection velocity is 2 ft/D, mCMC can increase the oil recovery by 5.79% compared with Partially hydrolyzed polyacrylamide (HPAM), and the viscoelasticity contributes 16.95% to the EOR. As the injection velocity increases, the EOR of HPAM levels off, but the EOR of mCMC still increases significantly, which increases the viscoelastic EOR contribution to 25.98% at 10 ft/D.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".