Applications of nanotechnology in oil and gas industry: Progress and perspective
Bibliographic record
Abstract
Abstract Nanotechnology has been successfully implemented in many applications, such as nanoelectronics, nanobiomedicine, and nanodevices. However, this technology has rarely been applied to the oil and gas industry, especially in upstream exploration and production. The oil and gas industry needs to improve oil recovery and exploit unconventional resources. The cost of research and oil production is under immense pressure, and it is becoming more difficult to justify such investment when the crude oil price is weak and depressed. There is a widespread belief that nanotechnology may be exploited to develop novel nanomaterials with enhanced performance to combat these technological barriers. Increasing funding resources from governmental and global oil industry have been allocated to exploration, drilling, production, refining, and wastewater treatment. For example, nanosensors allow for precise measurement of reservoir conditions. Nanofluids prepared using functional nanomaterials may exhibit better performance in oil production processes, and nanocatalysts have improved the efficiency in oil refining and petrochemical processes. Nanomembranes enhance oil, water and gas separation, oil and gas purification, and the removal of impurities from wastewater. Functional nanomaterials can play an important role in the production of smart, reliable, and more durable equipment. In this review paper, we summarize the research progress and prospective applications of nanotechnology and nanomaterials in the oil and gas 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".