High-Temperature Stable Dispersed Particle Gel for Enhanced Profile Control in Carbon Capture, Utilization, and Storage (CCUS) Applications
Bibliographic record
Abstract
CO 2 -responsive gels, which swell upon contact with CO 2 , are widely used for profile control to plug high-permeability gas flow channels in carbon capture, utilization, and storage (CCUS) applications in oil reservoirs. However, the use of these gels in high-temperature CCUS applications is limited due to their reversible swelling behavior at elevated temperatures. In this study, a novel dispersed particle gel (DPG) suspension is developed for high-temperature profile control in CCUS applications. First, we synthesize a double-network hydrogel consisting of a crosslinked polyacrylamide (PAAm) network and a crosslinked sodium alginate (SA) network. The hydrogel is then sheared in water to form a pre-prepared DPG suspension. To enhance its performance, the gel particles are modified by introducing potassium methylsilanetriolate (PMS) upon CO 2 exposure. Comparing the particle size distributions of the modified and pre-prepared DPG suspension reveals a significant swelling of gel particles, over twice their original size. Moreover, subjecting the new DPG suspension to a 100 °C environment for 24 h demonstrates that its gel particle sizes do not decrease, confirming irreversible swelling, which is a significant advantage over the traditional CO 2 -responsive gels. Thermogravimetric analysis further indicates improved thermal stability compared to the pre-prepared DPG particles. Core flooding experiments show that the new DPG suspension achieves a high plugging efficiency of 95.3% in plugging an ultra-high permeability sandpack, whereas the pre-prepared DPG suspension achieves only 82.8%. With its high swelling ratio, irreversible swelling at high temperatures, enhanced thermal stability, and superior plugging performance, the newly developed DPG suspension in this work presents a highly promising solution for profile control in high-temperature CCUS applications.
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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) | 0.001 | 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".