JuniperPublishers-Recent Advances in Petrochemical Science

Abstract We investigate the effect of size on the electronic and optical properties of zinc oxide(ZnO). CASTEP simulations based on density functional theory has been performed to find the band structure, density of states and optical properties of zinc oxide(ZnO) bulk and nanocluster. The results show quantum confinement effect as band structure show discrete lines. The change in band gap has been observed from 0.822 eV to 0.013 eV. Band structure calculations show Fermi level shift towards conduction band. Optical properties display low value of absorption in nanocluster as compared to bulk. ZnO nanocluster show high value of dielectric function compared to bulk. The results demonstrate high response of ZnO cluster in electromagnetic waves. Read more about this article:  https://scholar.google.com/citations?view_op=view_citation&hl=en&user=zngTjlYAAAAJ&cstart=100&pagesize=100&sortby=pubdate&citation_for_view=zngTjlYAAAAJ:V3AGJWp-ZtQC Read more   Juniper Publis

 

Juniper Publishers Abstract This paper reviews the recent challenges faced in oil and gas operations specifically the drilling and completion sector. The application of nanomaterials in drilling and completion operations in mitigating these challenges were analyzed as well as the factors retarding the growth of the application of this emerging technology. Introduction Nanomaterials are now a growing trend in this current age of technology. Its applications have cut across different kinds of sectors of the modern age industries. According to the US Foresight Institute, Nanomaterials can be defined as a group of emerging technologies in which the structure of matter is controlled at the nano scale to produce novel materials and devices that have useful and unique properties. This also brings about the creation of new materials with enhanced properties such as mechanically, optically, magnetically and many others. The oil and gas ha

Juniper Publishers Abstract Gas lift for oil-gas extraction is a common practice; however, obtaining maximum productivity of a series of set is not a simple tax because high amount of gas lift makes the optimization of few wells at the same time a very hard task. Therefore, data processing approaches based on calculations and computerized modeling has been receiving attention. By modeling well, it could be possible to obtain higher production rate versus less gas consumption. The present paper is a new approach that uses neural functions and genetic algorithm and studies the different aspects of problem solving for gas allocation optimization in five wells. The results showed that artificial neural networks have very good function in modeling gas lift process and creating gas lift performance curve versus classic methods. The differences between the results obtained by artificial neural network in comparison with results that are obtained in classic methods prove t