Stable Emulsion and Demulsification in Chemical EOR Flooding: Challenges and Best Practices
Bibliographic record
Abstract
Abstract In this paper, factors such as key issues (e.g. emulsions), challenges (e.g. water treatment), and best practices (e.g. combination of equipment design and demulsifier to treat emulsions) associated with chemical enhanced oil recovery (CEOR) are presented. During the application of chemical EOR floods, breakthrough of the injection chemicals such as surfactant and polymer or polymer alone periodically occurs resulting in stable emulsions. This paper was compiled from the literature to report the effects of alkali, polymers, surfactants, asphaltenes, resins, and shear rates on emulsion stability. When treated with polymer alone, the produced fluid was not very stable and resolved into two phases: oil and water. However, the water exhibited a high oily content and was difficult to treat due to the adsorption of polymer onto the surface of the oil droplets. Zeta potential measurements indicated that oil droplets were not only stabilized by steric stabilization of the polymer but also by electrostatic stabilization. The effect of polymer on emulsion stability in SP (surfactant and polymer) or ASP (alkali, surfactant, and polymer) floodings is complicated. Polymer can form a "bridge" between two oil droplets and decrease the emulsion stability; however, polymer can also enhance the emulsion stability via electrostatic and steric stabilization. Asphaltenes and resins present in the crude oil form a rigid film around water droplets, contributing to high BS&W values. Surfactants and alkali decrease the interfacial tension and zeta potential, contributing to the stability of oil droplets. As concentrations of the injection chemicals in the produced fluid varied, the stability of the emulsion also changed. As a result, the selected demulsifier has to be robust. In this paper, water soluble demulsifiers and oil soluble demulsifiers were used to treat the emulsion. The demulsifier greatly lowered water content in the oil phase (BS&W<0.5%) and oil concentration in the water phase (less than 50 ppm). The demulsification mechanism was also investigated in terms of elastic modulus, particle size, and interfacial tension. Application of this novel demulsifier resulted in a much more effective oil/water separations process with the production of dry oil and clean water at a pilot ASP flood that was experiencing very stable emulsions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".