Adsorption-Desorption-Related Interfacial Tension Behaviour in Chemical Flooding and Its Effect on Oil Recovery
Bibliographic record
Abstract
Abstract This paper reports the investigation of oil-water IFT behavior when the chromatographic separation of the surfactant mixture occurs during surfactant/alkaline corefloods. In this work, surfactant and alkaline concentrations in the effluent of corefloods and oil-water interfacial tension were determined underdifferent injection strategies. It was found that, in an extended waterflood following an alkaline-surfactant slug injection, surfactant desorbed into the water phase. This desorption of surfactant lasted for a long period of the waterflood. Although the concentration of the desorbed surfactant in the extended waterflood was very low, an ultra-low oil-water IFT was obtained by using a suitable alkaline concentration. Coreflood results showed that an additional 13% of the initial oil in place was recovered after the alkaline/surfactant injection by the synergism of the desorbed surfactant and alkaline. This result indicates that the efficiency and economics of a chemical flood could be improved by utilizing the desorbed surfactant during extended waterflood processes. Introduction In chemical flooding, surfactants are inevitably adsorbed on the surface of reservoir rock by the rock/oil/brine interaction. Surfactant adsorption is one of the important factors governing the economic feasibility of chemical flooding processes.1 Trogus et al.2 examined two aspects of the adsorption process: the rate and the amount of adsorption. They measured the dynamic adsorption of both anionic and nonionic surfactants on Berea cores that were initially saturated with brine. The relative adsorption levels for nonionic and anionic surfactants can be modeled by using a second-orderreversible rate expression that reduces a Langmuir-type adsorption isotherm at equilibrium. It has been shown that the nature of the adsorption isotherm depends to a large extent on the type of surfactant used, the morphological and mineralogical characteristics of the rock, and the type of electrolytes present in solution.3 The adsorption of surfactants can be affected by the surface charge on the rock surface and fluid interfaces.4, 5 Positively charged cationic surfactant will be attracted to negatively charged surfaces, while negatively charged anionic surfactants will be attracted to positively charged surfaces. The salinity and pH of brine strongly affect the surface charge.5, 6 When the effects of brine chemistry are removed, silica tends to adsorb simple organic bases (cationic surfactant), while the carbonates tend to adsorb simple organic acids (anionic surfactant). This occurs because silica normally has a negatively charged weak acidic surface in water near neutral pH, while the carbonates have positively charged weak basic surfaces. Several concerns about the chromatographic separation of the surfactant mixture have been expressed in the literature.7–12 Scamhorn et al.12 showed that adsorption is expected to increase with the surfactant's hydrophobicity at the pre-micellar concentration range of surfactant, since an increase in hydrophobicity tends to drive the surfactant from the aqueous phase to the solid-liquid surface. Mannhardt and Novosod 9 developed a model for adsorption of a surfactant mixture in flow through porous media. They concluded that the chromatographic movement of surfactant mixtures through the porous media depends not only on their affinity for the surface (selectivity), but also on their tendency to form micelles.
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 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".