Performance of Silica Nanoparticles in CO2 Foam for EOR and CCUS at Tough Reservoir Conditions
Bibliographic record
Abstract
Summary The use of nanoparticles for CO2-foam mobility is an upcoming technology for carbon capture, utilization, and storage (CCUS) in mature fields. Silane-modified hydrophilic silica nanoparticles enhance the thermodynamic stability of CO2 foam at elevated temperatures and salinities and in the presence of oil. The aqueous nanofluid mixes with CO2 in the porous media to generate CO2 foam for enhanced oil recovery (EOR) by improving sweep efficiency, resulting in reduced carbon footprint from oil production by the geological storage of anthropogenic CO2. Our objective was to investigate the stability of commercially available silica nanoparticles for a range of temperatures and brine salinities to determine if nanoparticles can be used in CO2-foam injections for EOR and underground CO2 storage in high-temperature reservoirs with high brine salinities. The experimental results demonstrated that surface-modified nanoparticles are stable and able to generate CO2 foam at elevated temperatures (60 to 120°C) and extreme brine salinities (20 wt% NaCl). We find that (1) nanofluids remain stable at extreme salinities (up to 25 wt% total dissolved solids) with the presence of both monovalent (NaCl) and divalent (CaCl2) ions; (2) both pressure gradient and incremental oil recovery during tertiary CO2-foam injections were 2 to 4 times higher with nanoparticles compared with no-foaming agent; and (3) CO2 stored during CCUS with nanoparticle-stabilized CO2 foam increased by more than 300% compared with coinjections without nanoparticles.
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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".