High-Temperature Stable Dispersed Particle Gel for Enhanced Profile Control in Carbon Capture, Utilization, and Storage (CCUS) Applications
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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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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 it