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.
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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".