Stability of Fluorosurfactant Adsorption on Mineral Surface for Water Removal in Tight Gas Reservoirs
Bibliographic record
Abstract
Long‐term effectiveness of rock wettability alteration for water removal during gas production from tight reservoir depends on the surfactant adsorption on the pore surface of a reservoir. This paper selected typical cationic fluorosurfactant FW‐134 as an example and took advantage of Fourier transform infrared spectroscopy (FTIR), X‐ray photoelectron spectroscopy (XPS), and atomic force microscope (AFM) to investigate its adsorption stability on the rock mineral surface under the oscillation condition at high temperature for a long time. The experimental results indicate that the F element content on the sample surface increases obviously, the surface structure of fluorine‐carbonization also undergoes a significant change, and the fluorine surfactant exhibits a good interfacial modification and wettability alteration ability due to its adsorption on the pore surface transforming the chemical structure of the original surface. The adsorption increases indistinctly with the concentration of over 0.05% due to a single layer adsorption structure and is mainly electrostatic adsorption because the chemical bonding between the fluorosurfactant and the rock mineral surface, the hydrogen bonding, is weak and inconspicuous.
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".