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 CO2 enhanced oil recovery and storage could see widespread deployment as decarbonization efforts accelerate to meet climate goals. CO2 is more efficiently distributed underground as a viscous foam than as pure CO2; however, most reported CO2 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 CO2‐phillicity, combined with the cationic surfactant N1‐alkyl‐N3, N3‐dimethylpropane‐1,3‐diamine (RCADA), will develop viscous, stable CO2 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 (θw) at the water–air and water–liquid CO2 interfaces. In a high‐temperature, high‐pressure flow apparatus, we calculated the viscosity of CO2 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 θw in liquid CO2 than in air. Addition of at least 0.5 μmol/m2 DM silane (0.5DM) greatly improved foam stability. RCADA‐only foam coarsening rates (dDSM3/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 μm3/min with 1 and 0.2 wt/vol% 8N3 + 1.5DM NP, respectively. These results demonstrate impressive foam stabilities at harsh reservoir conditions.
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 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".