Field Applications of Nanotechnology in the Oil and Gas Industry: Recent Advances and Perspectives
Why this work is in the frame
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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 (N2 and CO2) 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it