Applications of nanoparticle in enhanced oil recovery: A comprehensive review, history, and future prospects
Bibliographic record
Abstract
Abstract Oil extraction methods are categorized into three main stages: primary, secondary, and tertiary enhanced oil recovery (EOR). In the tertiary stage, techniques such as chemical injection, thermal injection, and dissolved gas injection are employed, with nanoparticles providing innovative solutions. Following primary and secondary recovery processes, more than 50% of the total oil volume remains trapped in reservoirs, highlighting the significance of EOR. Nanoparticles, ranging from 1 to 100 nanometres, enhance EOR through mechanisms such as permeability control, interfacial tension reduction, and mass transfer improvement. Among the nanoparticles studied, silica nanoparticles have shown extensive potential due to their stability and ability to alter reservoir wettability. These nanoparticles, along with others such as magnesium oxide, aluminium oxide, zinc oxide, and iron oxide, can increase the recovery factor by up to 20% by altering wettability, decreasing interfacial tension, and improving mobility control. The application of nanotechnology in the oil industry spans from exploration to refining, enhancing processes with nanomaterials such as solid compounds, complex fluids, and nanoparticle mixtures. Challenges include the high cost of chemicals and environmental concerns. The use of nanoparticles, particularly silica nanoparticles, in EOR demonstrates significant potential for improving oil extraction methods; however, it faces challenges in maximizing oil recovery while minimizing negative environmental impacts. Future research should focus on the application of nanotechnology in EOR to develop methods that are both effective and environmentally sustainable. Balancing efficiency and environmental responsibility are essential for advancing toward a cleaner and more efficient oil industry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".