Field Applications of Nanotechnology in the Oil and Gas Industry: Recent Advances and Perspectives
Bibliographic record
Abstract
During the early 21st century, nanotechnology has stood strong in the oil and gas industry, with many applications that have gone from laboratory and numerical simulation studies to successful trial applications in the field. In this Review, recent advances of nanofluid and nanoparticle applications in real environments of the oil and gas industry are presented. These applications cover more than 20 wells in Colombia that have been treated to overcome different formation damage mechanisms, such as asphaltene precipitation/deposition, fines migration, and inorganic scale deposition. Also, different approaches to enhance drilling fluids in Canada, Brazil, Iran, and Colombia are examined. In the case of improved oil recovery (IOR), different applications are discussed, including strategies to improve the productivity of heavy crude oil and extra-heavy crude oil reservoirs through enhanced mobility and hydraulic fracturing in Colombia, a field trial for water shutoff in Csongrad-3 formation in the Algyo field in Hungary, nanocapsules injection for wettability alteration, applications of gas injection (N 2 and CO 2 ) in the presence of nanoparticles in Austin chalk, Buda and Eagle Ford formations in the United States, and the use of nanoparticle-assisted foams for well dewatering in China. For secondary and tertiary recovery, we explore the design and implementation of A-Dots and carbon quantum dots as tracers in Saudi Arabia and Colombia, respectively, hydrophobic nanoparticles as drag reducers in injector wells in China, and nanofluids for enhancing chemical enhanced oil recovery processes in southern Colombia. It is worth mentioning that the results were based on oil production and reserves derived from production curves and analysis of the declination curves. Finally, challenges and perspectives of the role of nanotechnology in the oil and gas industry today are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".