Nanoparticles Technology for Improving Steam-Assisted Gravity Drainage Process Performance: A Review
Bibliographic record
Abstract
The increase in energy demand and the dynamics of the energy transition have changed the perspectives and opportunities within the oil and gas sector. This revolutionary scenario has resulted in sustainable and consolidated strategies, exemplified by the development of enhanced oil recovery (EOR) methods within the improved oil recovery (IOR) scheme, and specifically for this Review study in the steam-assisted gravity drainage (SAGD) techniques. The selection and impact of the EOR methods, as well as the expected recovery, rely on multiple factors, encompassing economic, environmental, and technological considerations. The above has led to the simultaneous appearance of uncertainties and difficulties that are continuously challenging the ranking of the oil sector in the global energy market. Against this background, nanotechnology has been established as a feasible strategy to be integrated into multiple stages of the oil supply chain. This Review study has focused on highlighting the synergistic trends of nanotechnology, steam, and oil recovery by conducting bibliometric analysis, where a tendency of the number of publications has increased in the last three years, as evidence of the application of nanotechnology in the oil industry. We have also carried out comprehensive discussions on the most widely implemented conventional thermal oil recovery methods with the dual purpose of explaining their methodologies and, at the same time, highlighting their inherent limitations and the imperative requirements to optimize them. In this direction, we have presented an in-depth exploration of the physicochemical mechanisms of action of nanoparticles to underscore the prospects for their applications in the EOR technologies. Finally, we contextualized the economic and technical feasibility of incorporating nanoparticles into industry, alongside advancements in simulation studies, presented as robust and cost-effective research alternatives, offering a more well-founded perspective on the scalability of nanoparticle technology.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".