A high temperature-resistant, strong, and self-healing double-network hydrogel for profile control in oil recovery
Bibliographic record
Abstract
Hydrogels are widely used in profile control to plug high-permeability zones in oil recovery. In this study, a novel double-network (DN) hydrogel is developed for profile control. The two networks of the prepared hydrogel are polyacrylamide (PAAm) crosslinked by N,N'-Methylenebisacrylamide (MBAA) and konjac glucomannan (KGM) crosslinked by borax (B), respectively. The two networks are interconnected by their interpenetrating structures and hydrogen bonds. Based on the results of a series of evaluation experiments, the AAm/KGM DN hydrogels developed in this study exhibit a strong mechanical strength with their fracture stresses exceeding 0.137 MPa. Meanwhile, the AAm/KGM DN hydrogels can remain thermally stable after being heated at 130 °C for 24 h, indicating the good high-temperature resistance of the new sample. Moreover, the prepared AAm/KGM DN hydrogels present excellent self-healing performance due to the abundant hydrogen bonds in their structures, which helps form stable and long-term plugging in porous media. In addition, the pure PAAm hydrogel and the AAm/KGM DN hydrogel are sheared into two dispersed particle gel (DPG) suspensions to investigate their plugging performances. The results demonstrate that the AAm/KGM DN DPG can effectively plug a high-permeability sandpack with a plugging efficiency of 93.2 %, while the pure AAm DPG can only provide a much lower plugging efficiency of 60.5 %. The AAm/KGM DN hydrogel developed in this study, with its high mechanical strength, high-temperature resistance, and self-healing capability, offers a promising new candidate for profile control in oil recovery.
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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 | 0.001 |
| 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".