Stable <scp>CO<sub>2</sub></scp>/water foam stabilized by dilute surface‐modified nanoparticles and cationic surfactant at high temperature and salinity
Bibliographic record
Abstract
Abstract CO 2 enhanced oil recovery and storage could see widespread deployment as decarbonization efforts accelerate to meet climate goals. CO 2 is more efficiently distributed underground as a viscous foam than as pure CO 2 ; however, most reported CO 2 foams are unstable at harsh reservoir conditions (22 wt% brine, 2200 psi, and 80°C). We hypothesize that silica nanoparticles (NP) grafted with (3‐trimethoxysilylpropyl)diethylenetriamine ligands (N3), to improve colloidal stability, and dimethoxydimethylsilane ligands (DM), to improve CO 2 ‐phillicity, combined with the cationic surfactant N 1 ‐alkyl‐ N 3 , N 3 ‐dimethylpropane‐1,3‐diamine (RCADA), will develop viscous, stable CO 2 foams at reservoir conditions. We grafted NP with N3 and DM ligands. We verified NP stability at reservoir conditions with measurements of zeta potential, amine titration curves, and NP diameter. We measured NP water contact angles ( θ w ) at the water–air and water–liquid CO 2 interfaces. In a high‐temperature, high‐pressure flow apparatus, we calculated the viscosity of CO 2 foams across a beadpack and determined static foam stability with microscope observations. Modified NP were colloidally stable at reservoir conditions for 4 weeks, and had higher θ w in liquid CO 2 than in air. Addition of at least 0.5 μmol/m 2 DM silane (0.5DM) greatly improved foam stability. RCADA‐only foam coarsening rates ( dD SM 3 / dt ) decreased 16–17× after adding 1 wt/vol% 8N3 + 1.5DM NP, and 5–10× with a 0.1–1 vol/vol% increase in RCADA concentration (with or without NP). 1 vol/vol% RCADA foam exhibited coarsening rates of 900 and 2400 μm 3 /min with 1 and 0.2 wt/vol% 8N3 + 1.5DM NP, respectively. These results demonstrate impressive foam stabilities at harsh reservoir conditions.
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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) | 0.001 | 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".