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[journal article]

dc.contributor.authorBaaziz, Abdelkaderde
dc.contributor.authorQuoniam, Lucde
dc.date.accessioned2015-11-16T12:51:54Z
dc.date.available2015-11-16T12:51:54Z
dc.date.issued2013de
dc.identifier.issn2318-9975de
dc.identifier.urihttp://www.ssoar.info/ssoar/handle/document/45350
dc.description.abstract"Big Data is the oil of the new economy" is the most famous citation during the three last years. It has even been adopted by the World Economic Forum in 2011. In fact, Big Data is like crude! It's valuable, but if unrefined it cannot be used. It must be broken down, analyzed for it to have value. But what about Big Data generated by the Petroleum Industry and particularly its upstream segment? Upstream is no stranger to Big Data. Understanding and leveraging data in the upstream segment enables firms to remain competitive throughout planning, exploration, delineation, and field development. Oil & Gas Companies conduct advanced geophysics modeling and simulation to support operations where 2D, 3D & 4D Seismic generate significant data during exploration phases. They closely monitor the performance of their operational assets. To do this, they use tens of thousands of data-collecting sensors in subsurface wells and surface facilities to provide continuous and real-time monitoring of assets and environmental conditions. Unfortunately, this information comes in various and increasingly complex forms, making it a challenge to collect, interpret, and leverage the disparate data. As an example, Chevron's internal IT traffic alone exceeds 1.5 terabytes a day. Big Data technologies integrate common and disparate data sets to deliver the right information at the appropriate time to the correct decision-maker. These capabilities help firms act on large volumes of data, transforming decision-making from reactive to proactive and optimizing all phases of exploration, development and production. Furthermore, Big Data offers multiple opportunities to ensure safer, more responsible operations. Another invaluable effect of that would be shared learning. The aim of this paper is to explain how to use Big Data technologies to optimize operations. How can Big Data help experts to decision-making leading the desired outcomes?en
dc.languageende
dc.subject.ddcWirtschaftde
dc.subject.ddcSociology & anthropologyen
dc.subject.ddcEconomicsen
dc.subject.ddcNews media, journalism, publishingen
dc.subject.ddcPublizistische Medien, Journalismus,Verlagswesende
dc.subject.ddcSoziologie, Anthropologiede
dc.subject.otherBig Data; analytics; upstream petroleum industry; business intelligence; decision-making under uncertaintyde
dc.titleHow to use Big Data technologies to optimize operations in Upstream Petroleum Industryde
dc.description.reviewbegutachtet (peer reviewed)de
dc.description.reviewpeer revieweden
dc.source.journalInternational Journal of Innovation
dc.source.volume1de
dc.publisher.countryMISC
dc.source.issue1de
dc.subject.classozInformation Management, Information Processes, Information Economicsen
dc.subject.classozWissenschaftssoziologie, Wissenschaftsforschung, Technikforschung, Techniksoziologiede
dc.subject.classozInformationsmanagement, informationelle Prozesse, Informationsökonomiede
dc.subject.classozSociology of Science, Sociology of Technology, Research on Science and Technologyen
dc.subject.classozEconomic Sectorsen
dc.subject.classozWirtschaftssektorende
dc.subject.thesozGrundstoffindustriede
dc.subject.thesozbasic industryen
dc.subject.thesozErdölde
dc.subject.thesozcrude oilen
dc.subject.thesozErdgasde
dc.subject.thesoznatural gasen
dc.subject.thesozWissensmanagementde
dc.subject.thesozknowledge managementen
dc.subject.thesozDatengewinnungde
dc.subject.thesozdata captureen
dc.subject.thesozDatende
dc.subject.thesozdataen
dc.subject.thesozNutzungde
dc.subject.thesozutilizationen
dc.subject.thesozMonitoringde
dc.subject.thesozmonitoringen
dc.subject.thesozWettbewerbsfähigkeitde
dc.subject.thesozcompetitivenessen
dc.rights.licenceCreative Commons - Namensnennung, Weitergabe unter gleichen Bedingungende
dc.rights.licenceCreative Commons - Attribution-ShareAlikeen
internal.statusformal und inhaltlich fertig erschlossende
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dc.type.stockarticlede
dc.type.documentZeitschriftenartikelde
dc.type.documentjournal articleen
dc.source.pageinfo19-25de
internal.identifier.classoz10220
internal.identifier.classoz1080502
internal.identifier.classoz1090304
internal.identifier.journal799
internal.identifier.document32
dc.rights.sherpaGrüner Verlagde
dc.rights.sherpaGreen Publisheren
internal.identifier.ddc070
internal.identifier.ddc330
internal.identifier.ddc301
dc.identifier.doihttps://doi.org/10.5585/iji.v1i1.4de
dc.description.pubstatusVeröffentlichungsversionde
dc.description.pubstatusPublished Versionen
internal.identifier.sherpa1
internal.identifier.licence8
internal.identifier.pubstatus1
internal.identifier.review1
dc.subject.classhort10800de
dc.subject.classhort29900de
dc.subject.classhort20800de
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internal.check.abstractlanguageharmonizerCERTAIN


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