Nonionic Surfactant for Enhanced Oil Recovery from Carbonates: Adsorption Kinetics and Equilibrium
Bibliographic record
Abstract
Around 40% of the current world conventional oil production comes from carbonate reservoirs, dominantly mature and declining giant oilfields. Tertiary oil production methods as part of an Enhanced Oil Recovery (EOR) scheme are inevitable after primary and secondary oil production. The goal of surfactant flooding is to reduce the mobility ratio by lowering the interfacial tension between oil and water and mobilizing the residual oil. This paper highlights adsorption kinetics and equilibrium of Glycyrrhiza Glabra, a novel surfactant, in aqueous solutions for EOR and reservoir stimulation purposes. A conductivity technique was used to assess adsorption of the surfactant in the aqueous phase. Batch experimental runs were also performed at various temperatures to understand the effect of adsorbate dose on the sorption efficiency. The adsorption kinetics was experimentally investigated at room temperature (27 °C) by monitoring the uptake of the Glycyrrhiza Glabra as a function of time. The adsorption data were examined using different adsorption equilibrium and kinetic models. The Langmuir isotherm suits the equilibrium data very well. A pseudo-second order kinetic model can satisfactorily estimate the kinetics of the surfactant adsorption on carbonates. Results obtained from this research can help in selecting appropriate surfactants for design of EOR schemes and reservoir stimulation plans for carbonate reservoirs.
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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 | 0.001 | 0.001 |
| 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 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".