A critical review on nanoparticle-assisted enhanced Oil recovery: Introducing scaling approach
Bibliographic record
Abstract
Nanotechnology has the capability to modernize both the upstream and downstream oil and gas industry. It has been effectively used in exploration, drilling, production, refinery as well as in enhanced oil recovery (EOR) fields. Understanding the basics of scaling criteria development along with nanoparticle stabilized EOR mechanism will assist petroleum engineers in designing, analyzing, and evaluating nanoparticle-assisted EOR techniques. This paper aims to deliver a critical review on nanoparticle-assisted EOR methods along with introducing scaling approaches and their applications in EOR. Scaling criteria can be employed to assess the performance of a specific EOR technique so that it can be accurately applied to the field scale. In this study, scaling criteria or dimensionless approaches are briefly summarized along with their applications in EOR. In addition, it reviews how scaling criteria can be derived using a mathematical model along with their benefits and shortcomings. This work concentrates on assessing the application of nanoparticles in EOR processes and addresses the process controlling parameters. This study briefly evaluates a few appropriate analytical and semi-analytical studies directly related to nanoparticle-assisted EOR techniques. Several nanoparticles assisted experimental works have been reviewed for both core flooding and micromodel systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".