Effects of asphaltenes and molecular composition on the adsorption mechanisms of surfactant mixtures onto basal sandstone of the carbonera formation
Bibliographic record
Abstract
The adsorption of surfactants from aqueous solutions in porous media is a determining parameter in enhanced oil recovery processes. Surfactant loss due to adsorption on the reservoir rocks is associated to an efficiency decrease during traditional chemical flooding, as it diminishes the reduction of oil–water interfacial tension. However, in the case of oilfields stimulations by cyclic injections of surfactant formulations, adsorption can lead to surface wettability changes. Natural crude-oil polar molecules, such as asphaltenes adsorbed on the mineral are associated to oil-wet surfaces. In such cases, surfactants molecules can compete with asphaltenes for adsorption onto the porous medium or being adsorbed onto these molecules by hydrophobic interactions. Traditional studies focus on the static adsorption isotherms of solid/surfactants systems. This study investigates the adsorption behavior of anionic/nonionic surfactant mixtures including asphaltenes molecules, focusing on the influence of molecular composition and critical micelle concentration. Three surfactant formulations were evaluated: two highly hydrophilic formulations, each composed of two molecules with significantly different water affinities, and one formulation with reduced hydrophilicity. Adsorption mechanisms were analyzed across concentration ranges, with particular attention to monomeric and micellar regions. Results revealed that adsorption behavior is governed by the surfactants’ hydrophilicity and interactions with the substrate or asphaltenes. In the hydrophilic Formulation 1, asphaltenes had minimal impact on total adsorption, but selective adsorption of anionic surfactants on asphaltenes was observed. In contrast, the less hydrophilic Formulation 2 exhibited reduced adsorption in the presence of asphaltenes, with enhanced interfacial excess below the critical micelle concentration attributed to electrostatic and hydrophobic interactions. Finally, the hydrophilic Formulation 3 showed significantly increased adsorption in the presence of asphaltenes due to the reduced water affinity of its components. These findings suggest that formulations with balanced hydrophilicity and molecular interactions can enhance adsorption stability and maintain interfacial tension, providing valuable insights for optimizing surfactant performance in enhanced oil recovery applications.
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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".