Chemically crosslinked polyvinyl alcohol for water shut‐off and conformance control treatments during oil production: The effect of silica nanoparticles
Bibliographic record
Abstract
Abstract Polyvinyl alcohol (PVA) is a synthetic commercial polymer with the inherent hydrophilicity, thermal and chemical resistance, ecofriendly, and a high anti‐fouling potential making it an attractive choice for water treatment applications, but has been less studied for oil and gas industry applications. On the other hands, nowadays nanotechnology has gained an important space within most core areas in upstream oil and gas operations. The present work, first PVA at various concentrations, was added to 5 wt% NaCl solution and then, crosslinked by formaldehyde 37% at two different concentration ratios. To compare, a nanocomposite hydrogel was fabricated in the same way with 1 wt% silica nanoparticles (NPs). Contact angle and filtration test were performed to confirm the ability of PVA hydrogel and nanocomposite hydrogel for oil and water adsorption. Following this, a rheology measurement was made to realize the gelation time of samples and their performance for water shutoff applications. Finally, an experimental flooding setup was designed to inject the fluids into carbonate plugs in order to estimate of oil and water effective permeability, and oil recovery factor (RF) before and after the PVA hydrogel and nanocomposite hydrogel injection. Both samples wettability tests showed a super‐hydrophilic state for brine droplets and neutral state for synthetic oil droplets by using nanocomposite hydrogel. The flooding tests revealed that the PVA hydrogel was clogged the plug with blocking efficiency of 32.83% for water effective permeability and 14.60% for oil effective permeability. This value was calculated to be 50.37% for water effective permeability and 31.36% for oil effective permeability in the case of nanocomposite hydrogel injection. Oil RF was also reported to be 64.58% after injecting PVA hydrogel which was higher than nanocomposite hydrogel injection with RF of 52.08%.
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 | 0.001 | 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".