Prevention of network destruction of partially hydrolyzed polyacrylamide (HPAM): Effects of salt, temperature, and fumed silica nanoparticles
Bibliographic record
Abstract
Polymer flooding is one of the most effective enhanced oil recovery (EOR) methods. High temperature and a high salt content in oil reservoirs significantly decrease the performance of polymer flooding. In this work, the viscoelastic properties of a partially hydrolyzed polyacrylamide (HPAM) solution with and without salt (NaCl) and at two different temperatures (35 °C and 70 °C) were evaluated using rheological approaches. Two fumed silica nanoparticles (NPs) featuring different surface chemistries were used, and their ability to prevent destruction of the polymer network structure against salt addition and temperature increase was investigated. Linear rheological tests (frequency sweep, creep, and creep recovery) and nonlinear rheological tests (large amplitude oscillatory shear) were employed to evaluate the network structure of these systems. The results showed that either adding salt or increasing the temperature destroyed the mechanical integrity of the HPAM 3-dimensional elastic network. However, the introduction of both types of NPs at a sufficient concentration maintained the network structure of HPAM solutions in the small deformation region. In the large deformation region, it was shown that the extent of intra-cycle shear-thickening behavior in the HPAM solution (T = 35 °C and without any salt) decreased by incorporating salt or by increasing the temperature. Moreover, upon incorporating either of the NPs to the HPAM solution, the nonlinear viscoelastic behavior dramatically changed, and the critical strain (linear to nonlinear transition) decreased to a much lower strain amplitude. The outcomes of this study will help petroleum scientists to design more efficient EOR methods.
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 | 0.000 |
| 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 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".