Pic pétrolier

Résumé

thumb|upright=1.6|Courbes cumulées de production de pétrole (graphique de 2005). À cette époque, le pic pétrolier était attendu pour l'année 2006, année où le pétrole conventionnel a effectivement atteint son pic. Toutefois, l'essor du pétrole de schiste américain dans les années 2010 a repoussé les perspectives du pic pétrolier mondial vers 2025. Le pic pétrolier (ou « peak oil » en anglais) est le sommet de la courbe de l'extraction mondiale de pétrole (calculée en millions de barils par jour), autrement dit le point où celle-ci atteint son niveau maximal avant de connaître par la suite un déclin dû à l'épuisement progressif des réserves de pétrole contenues dans le sous-sol terrestre. Le terme peut également être utilisé à une échelle plus locale pour désigner le pic de production d'un champ pétrolifère ou d'un pays producteur, dont la courbe d'exploitation peut également avoir tendance à suivre une forme en cloche (courbe de Gauss). Marion King Hubbert a été le premier géologue

Source officielle

https://fr.wikipedia.org/wiki/Pic_p%C3%A9trolier

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'Peak Oil' As Classical Economic Process

François Meynard

Forecasts about 'peak oil', the limits of oil production, are the subject of controversies. 'Peak oil' forecasts are based on extrapolations of a priori chosen mathematical models fitted to field data. Scientifically that is not very sound. To clarify this point, in this paper 'peak oil' is modelled in a generic reductionist, approach, in which the conflicting dynamic between economic forces and physical laws are well identified. This model finds a natural interpretation just supress in a classical economic analysis frame. The 'peak oil' controversy is thus brought into the field of economic thought as a debate between classical and mainstream economics, in particular between an objective and a faith based appraisal about this historical event. The goal is not to improve forecasts about 'peak oil', which is very likely not a predictable event, but to propose an objective framework for the analysis of its systemic effects on economic production.

Multi-Science Publ Co Ltd2014

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Statistical physics of vaccination

Marcel Salathé, Zhen Wang

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination one of the most important preventive measures of modern times is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research. (C) 2016 Elsevier B.V. All rights reserved.

Elsevier Science Bv2016

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Peak Oil & Iran

Mohammadjavad Faraji, Ramtin Pedarsani

2010

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