Nanopyroxene-Based Nanofluids for Enhanced Oil Recovery in Sandstone Cores at Reservoir Temperature
Bibliographic record
Abstract
Nanoparticles (NPs) have recently gained great attention as effective agents for enhanced oil recovery (EOR) applications, especially at ambient temperatures. Nevertheless, harsh conditions are needed to synthesize them and many tend to lack stability, exhibiting strong limitations in EOR application. As a solution, many researchers have used silica nanomaterials and grafted them with various agents to enhance their stability and alter their functionality. However, altering their overall functionality via coating could limit their stability in an aqueous media. Thus, partially coated nanoparticles should be used, such that the functionalizing agent is bonded to certain active groups on the nanoparticle surface. Herein, environmentally safe and tunable silicate-based nanoparticles, nanopyroxene, were synthesized under mild conditions. Nanopyroxene consists of two forms of binding sites on the surface made of the structural iron framework imparting a negative charge on the surface, which is compensated by sodium ions and the hydroxyl groups present on the surface of the silicate framework, which is responsible for its hydrophilicity. In this study, we partially altered the functionality of the nanopyroxene by anchoring a hydrophobic functionalizing agent of triethoxy(octyl)silane to the hydroxylated binding sites, generating half and totally hydroxyl functionalized nanopyroxene, such as hydrophobic pyroxenes (HPNPs) and Janus pyroxenes (JPNPs), respectively. Characterization techniques, such as scanning electron microscopy, Fourier transform infrared, X-ray diffraction, thermal gravimetric analysis, dynamic light scattering, and ζ-potential were conducted for the produced NPs to confirm their surface identity, functionality, stability, and morphology. After that, in comparison with brine, three different nanofluids were generated from the synthesized NPs to test their performance toward EOR in sandstone cores. The EOR performance was investigated by interfacial tension, contact angle, spontaneous imbibition, and displacement tests. The results showed that the HPNPs have the best stability and functionality compared with the other nanoparticle types. Contact angles in the presence of NPNP, JPNP, and HPNP nanofluids were measured to be 44 ± 2, 50 ± 2, and 55 ± 2°, respectively, confirming wettability alteration from oil-wet to water-wet. Interfacial tension was also noticeably reduced with the produced nanofluids at all temperatures, showing their great potential of oil displacement and to prove that a core flooding test was performed, confirming the capability of the stable nanoparticles as effective EOR agents in hydrocarbon reservoirs by recovering an additional 10.57% after brine flooding.
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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".