Formulation of Spontaneous In Situ Emulsification Using Sodium Lauryl Sulfate Grafted Nanopyroxene for Enhanced Heavy Oil Recovery in Sandstone Reservoirs
Bibliographic record
Abstract
Heavy oil recovery presents enormous challenges during production, especially in thin reservoirs, where thermal recovery is inefficient. To overcome these challenges, chemically heavy oil recovery methods are extensively used. Herein, we formulated a new class of stable nanofluids from surfactant-coated nanomaterials to improve the microscopic displacement efficiency and recover a medium-viscosity heavy oil. Our nanofluid consists of an anionic surfactant, sodium lauryl sulfate (SLS), grafted on the surface of nanopyroxene to be used as an emulsifier that can instinctively emulsify heavy oil by minimal agitation designed for heavy oil enhanced oil recovery (EOR). Optimum screening of the designed emulsions was performed by interfacial tension (IFT) measurements and emulsion stability testing. The droplet size distribution and microscopic morphology of the created emulsions were observed by an optical microscope, dynamic light scattering testing, and magnetic resonance imaging. Afterward, the EOR mechanism of emulsions was investigated by core flooding studies with the aid of NMR and/or computer tomography (CT). The characterization results showed that our synthesized nanoparticles were successfully grafted with the anionic surfactant. Then, the grafted surfactant-nanopyroxene resulted in an ultralow IFT and hence stable oil/water emulsions (E1). Moreover, the synergy effect between nanopyroxene and SLS was further enhanced by adding 0.2 wt % NaOH, which greatly improved the capability of emulsification (E2). As a result, the designed formulation E1 and/or E2 emulsified heavy crude oil by applying a minimum force. Notably, crude oil in small pores was more effectively displaced by the E2 system than E1. Consequently, E2 exhibited a higher EOR efficiency than the E1, SLS, and SLS+NaOH systems. E2 recovered (34.4%) compared to E1 (18.5%), SLS (16.2%), and SLS+NaOH (1.2%). This work revealed the EOR mechanism of the surfactant grafted nanoparticle systems from the level of the pore structure using NMR and CT scan.
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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 | 0.000 |
| 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 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".