Effect of Various Classes of Surfactants on Interfacial Tension Reduction and Wettability Alteration on Smart-Water-Surfactant Systems
Bibliographic record
Abstract
In recent times, smart water flooding, capable of altering rock’s wettability, has been widely recognized as a potential enhanced oil recovery (EOR) method for low-permeable, mixed/oil-wet carbonate reservoirs. Smart water flooding does not provide significant interfacial tension (IFT) reduction; however, when combined with a surfactant, the synergetic effect can provide favorable IFT reduction and wettability alteration for improved oil recovery in tight carbonate reservoirs. The IFT and wettability altering effect during the addition of various classes of surfactants to smart water flooding at a broader range of salinity have not been investigated. In this work, the electrokinetic interaction between oil, three different surfactant solutions prepared in different brines in two different tight carbonate rocks is studied using zeta sizer nano (the equipment used to measure the zeta potential of the oil–brine and rock–brine systems). Cationic, anionic, and nonionic surfactants were used in this study. Initially, three different high-salinity brines were diluted to seven different concentrations for surfactant solution preparation. Among the seven different concentrations ranging from 0 to 200,000 ppm, it was observed that surfactant solutions prepared in 5000, 25,000, 50,000, and 100,000 ppm brine concentrations provided a significant effect on fluid-rock and fluid–fluid interactions. Therefore, surfactant solutions prepared in these brine concentrations were subjected to direct IFT measurements. For nonionic and cationic surfactants at all brine types investigated, the IFT is the lowest at 100,000 ppm brine salinity, corresponding to the lowest zeta potential magnitude. Additionally, anionic surfactants outperform the wettability alteration capabilities of other surfactants in NaCl and synthetic formation brines (SFMBs) while cationic surfactants were observed to be better in CaCl 2 brine. Although the nonionic surfactant reduces the IFT better compared to the other two surfactants, its effect on wettability alteration is adverse.
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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".