Recent progress in NP-Based Enhanced oil Recovery: Insights from molecular studies
Bibliographic record
Abstract
In recent decades, nanotechnology has emerged as a rapidly growing field with diverse applications in industries such as pharmaceuticals, energy, and engineering. One of the key areas of interest is the use of nanoparticles (NPs) and nanofluids (NFs) in Enhanced Oil Recovery (EOR) to improve oil recovery efficiency. NPs offer several benefits in the hydrocarbon industry and have been shown to enhance oil and gas production. In EOR, NPs play a crucial role by interacting with the rock/oil system, optimizing conditions for oil retrieval. They offer a cost-effective and eco-friendly alternative compared to conventional methods. This comprehensive study delves into the diverse range of NPs and nanomaterials utilized in the petroleum industry, detailing their classification, characterization, and inherent properties. It explores multiple applications of NPs in chemical, thermal, and microbial flooding, elucidating the involved mechanisms like wettability modification and mobility control. Moreover, the research examines the utilization of NPs in EOR through image-based modeling, a groundbreaking approach in enhancing EOR techniques. It highlights the considerable progress achieved in EOR through the application of NPs and nanofluids (NFs). Additionally, the study assesses the potential of NPs in image-based modeling and their implications for future EOR applications, indicating a promising trajectory for integrating NPs into the petroleum 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".