Effect of Sodium Dodecyl Benzene Sulfonate on Water-Soluble Hydrophobically Associating Polymer Solutions
Bibliographic record
Abstract
Abstract The work presented in this paper focuses on the effect of sodium dodecycl benzene sulfonate (SDBS) on the rheological behaviour of specific-modified hydrophobically associating polymer (HAP) aqueous solutions. The viscosities of polymer solutions from dilute, unentangled semi-dilute, and entangled semi-dilute regimes were examined as a function of shear rate and SDBS concentration. It was found that SDBS enhanced the shear thickening in the entangled semi-dilute regime. The result was explained in terms of the balance between inter- and intrachain liaisons and the effect of SDBS on the chain dimensions of the polymer. Fluorescent data showed that SDBS was associated with the hydrophobic groups of the polymer, generating a number of microdomains. These microdomains strengthened the structure viscosity of the polymer solutions. The addition of SDBS to the HAP solution also helped build up a reversible molecular structure and even a temporarily threedimensional network in aqueous solutions via binding SDBS with hydrophobes of the polymer chains. The network structure resulted in a significant increase in viscosity of the polymer solutions. Consequently, the SDBS/HAP systems could be used as thickeners for the fluids used in EOR processes. Introduction Water-soluble polymers are widely used in oilfield operations such as drilling, polymer flooding, chemical flooding, and profile modification. Water-soluble hydrophobically associating polymers (HAP) are similar to conventional water-soluble polymers except that a very small portion of hydrophobic comonomer (<1%)(1,2) is incorporated into the polymer backbone. Above the overlap concentration, these polymer chains associate intermolecularly in solution, significantly increasing hydrodynamic size and inherent viscosity. This is of great importance in enhanced oil recovery (EOR). Interactions between HAP and surfactants have been well documented(2–5). The addition of surfactants to polymer solutions can influence the microenvironment of both components. Surfactants associate with the polymer or form micelles. Meanwhile, the polymer chains tend to re-conform to associate with the surfactant. It has been commonly accepted that such systems form hydrophobic microdomains(3,4) through the association of surfactants on hydrophobes of polymers, which cause the polymer chains to extend or prevent association of polymer and surfactant by forming micelle around the pendant groups(5). The most obvious change after adding surfactants to HAP is the variation of viscosity of the polymer solution(3), which responds tothe re-conformation of the polymer chains. It is often assumed that the viscosity of plastic fluid consists of structure viscosity and non-structure viscosity(6). Non-structure viscosity is determined by the hydrodynamic size of a single molecule, whereas structure viscosity is controlled by the size and strength of the three-dimensional network. Compared to the non-structure viscosity, structure viscosity is more easily influenced by the shear stress. Polymer solutions could be divided into dilute, unentangled semi-dilute, entangled semi-dilute, and high concentration regimes according to the types of HAP molecular associations. The Fluorescence Probe method has been increasingly important in elucidating surfactant-polymer interactions and monitoring hydrophobic domain formation in polymers bearing hydrophobic side chains(2,3,8–10). Pyrene is the most commonly used probe because it has extremely low solubility in water and can be solubilized by hydrophobic clusters, such as micelles or associating polymers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".