Applications of Nanotechnology in Oil and Gas E&P
Bibliographic record
Abstract
Special Feature: Applications of Nanotechnology in Oil and Gas E&P Saeid Mokhatab, U. of Wyoming; Mariela Araujo Fresky, Imperial College, London; and M. Rafiqul Islam, Dalhousie U. Over the next 30 years, global energy demand is projected to rise as high as almost 60%, a challenging trend that may be met only by revolutionary breakthroughs in energy science and technology. The industry needs stunning discoveries in underlying core science and engineering. Breakthroughs in nanotechnology open up the possibility of moving beyond the current alternatives for energy sup-ply by introducing technologies that are more efficient and environmentally sound. Nanotechnology is characterized by collaboration among diverse disciplines, making it inherently innovative and more precise than other technologies. Such a technology may be the cornerstone of any future energy technology that offers the greatest potential for innovative solutions. "Nano" denotes a thousand millionths (10−9), with a nanometer equaling a millionth of a millimeter. That corresponds to the width of 10 hydrogen atoms. So the nanotechnologist is concerned with building new structures and substances by manipulating molecules and atoms on this scale. Technically, nanotechnology is the art and science of building materials that act at the nanometer scale. It builds at the ultimate level of finesse, one atom at a time, and it does it with molecular perfection. In a general sense, nanotechnology is the ability to create and manipulate matter at the molecular level that makes it possible to create materials with improved (or, more accurately, altered) properties, such as being both lightweight and having ultrahigh strength, and greater capabilities such as in electrical and heat conductivity. Another research approach is known as top-down nanofabrication, which involves working with bulk materials and reducing them to nanometer size. This is most common in currently used technology development schemes. Nanotechnology is exciting because the science and engineering behind it are largely unknown. In fact, most scientists are aware that the laws that govern materials at nanoscale are very different from those that have been widely accepted in larger scales (Islam 2004). Recent work by Nobel Prize physicist Richard Smalley of Rice U. supports this hypothesis. He discovered that carbon nanotubes (Fig. 1) and fullerenes (buckyballs), nanoparticles of carbon, which are conventionally characterized as graphite, behaved in ways unlike graphite (Smalley and Yakobsonb 1998; Zhou et al. 2005). Potential Benefits Scientific inquiry in the energy area is scattered and unfocused, with various groups working separately to gain research dollars for uncoordinated pursuits that lack a clear road map to a better energy future (Baker Inst. Study 2005). But nanotechnology is poised to impact dramatically on all sectors of industry (Fig. 2). In oil and gas applications, nanotechnology could be used to increase opportunities to develop geothermal resources by enhancing thermal conductivity, improving downhole separation, and aiding in the development of noncorrosive materials that could be used for geothermal-energy production. Nanoscale metals already have been used to delineate ore deposits for geochemical exploration (Wang et al. 1997).
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.027 |
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".