Effects of Surface Acidity and Polarity of SiO<sub>2</sub>Nanoparticles on the Foam Stabilization Applied to Natural Gas Flooding in Tight Gas-Condensate Reservoirs
Bibliographic record
Abstract
Foams in the oil and gas industry have been used as divergent fluids to attenuate the fluid channeling in high-permeability zones. Commonly, foams are generated using a surfactant solution in high-permeability reservoirs, which exhibit stability problems. Therefore, the main objective of this study is to stabilize the foams by the addition of modified silica nanoparticles, varying the surface acidity and polarity for natural gas flooding in tight gas-condensated reservoirs. Four types of modified silica-based nanoparticles with varying surface acidity and polarity (coated with vacuum residue) were synthesized and evaluated using surfactant adsorption. The basic nanoparticles exhibited a greater adsorption capacity of the surfactant, reaching an adsorbed amount of approximately 200 mg of surfactant per gram of nanoparticles, and Type I adsorption behavior. Foams were generated and evaluated based on their stability using two routes, namely, (1) with mechanical agitation and (2) methane flooding, to determine the optimal concentration of nanoparticles to be used. In both scenarios, foam height was monitored against time, and the half-life of the foam was established. The nanofluid prepared using a surfactant solution and 500 mg/L of basic nanoparticles reached a half-life 41% greater than that of the fluid that does not contain nanoparticles. In addition, a core flooding test was performed to evaluate the generation and perdurability of the foam (with and without nanoparticles) by methane flooding and the mobility reduction at typical reservoir conditions (confinement and pore pressure of 5200 and 1200 psi, respectively, and temperature of 100 °C). The porous medium was obtained from a tight gas-condensate reservoir, and it has an absolute permeability of 65.1 mD and a porosity of 7%. The oil recovery with methane injection was about 52%; with foam injection, an additional 10% was obtained, and an 18% additional recovery was reached with the injection of foam and nanoparticles.
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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 | 0.000 | 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) | 0.000 | 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".