Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Foamed fluids have been used for decades to diminish formation damage in nearly all kinds of reservoirs over a wide range of pressures and temperatures. Although water-based fluids are widely used in the oil industry as one of the most-economic hydraulic-fracturing methods, foam is another viable alternative to fracture water-sensitive reservoirs where damage to pore throats is caused by swelling clays or fines migration. CO2 foam not only reduces formation damage by minimizing the quantity of aqueous fluid that enters the formation, but also significantly improves sweep efficiency. Even though surfactant is commonly used to generate stable foam in high-temperature and high-salinity environments, such foam can degrade in these harsh conditions. The main objective of this study is to improve the stability of CO2 foam by the use of a mixture of CO2/alpha olefin sulfonate (AOS) solution with nanoparticles, guar gum, or viscoelastic surfactants (VESs). Foam stability is studied for various solutions by the use of a high-pressure view-chamber (HPVC) setup to find the optimal surfactant and nanoparticle concentration at which higher foam stability in the CO2 foam system can be reached. In addition to surfactant and nanoparticle concentration, the effects of temperature, pressure, and salinity on foam stability were studied. Temperature ranged from 75 to 212°F, and pressure increased from atmospheric to 800 psi. AOS solutions were prepared with brine and surfactant concentrations ranging from 1.0 to 10 wt% of NaCl and zero to 1 wt% of AOS. Temperature and pressure had a negative effect on the foam stability when AOS solutions were used. However, nanoparticles improved the foam stability for AOS, AOS and guar gum, and AOS with VES solutions.
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.
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 | 0.001 |
| 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 it