dc.contributor.author | Almklov, Petter Grytten | |
dc.contributor.author | Østerlie, Thomas | |
dc.contributor.author | Haavik, Torgeir K | |
dc.date.accessioned | 2017-10-02T06:53:58Z | |
dc.date.available | 2017-10-02T06:53:58Z | |
dc.date.created | 2012-06-18T10:02:30Z | |
dc.date.issued | 2012 | |
dc.identifier.citation | Journal of experimental and theoretical artificial intelligence (Print). 2012, 24 (3), 329-350. | nb_NO |
dc.identifier.issn | 0952-813X | |
dc.identifier.uri | http://hdl.handle.net/11250/2457597 | |
dc.description.abstract | This article discusses how data are made to represent subsurface phenomena in petroleum production. Drawing on studies of the subsurface disciplines in an oil company, and the multitude of sensor data employed there, we suggest that sensor data as representational artifacts are punctuated along three axes. We refer to this as spatial, temporal and aspectual punctuation. Whereas, the first two refer to the positioning of data in space and time, the latter refers to the sensors’ response to single aspects of the interaction with a subsurface phenomenon. We show how extrapolation of punctuated data is a crucial element of the work of understanding the subsurface. It is when the punctuated data points are creatively extrapolated along the three axes of punctuation that ideas and models of the subsurface phenomena take shape. Consequently, we argue that the processes of punctuation and extrapolation are the keys to understand how knowledge about the subsurface is created at the onshore office. Punctuation gives mobility whereas extrapolation is necessary to establish reference between the punctuated data and the inaccessible oil reservoir. We specifically discuss the implications this has for reservoir models as representational artifacts. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Taylor & Francis | nb_NO |
dc.title | Punctuation and extrapolation: representing a subsurface oil reservoir | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | submittedVersion | nb_NO |
dc.source.pagenumber | 329-350 | nb_NO |
dc.source.volume | 24 | nb_NO |
dc.source.journal | Journal of experimental and theoretical artificial intelligence (Print) | nb_NO |
dc.source.issue | 3 | nb_NO |
dc.identifier.doi | 10.1080/0952813X.2012.695448 | |
dc.identifier.cristin | 929980 | |
dc.relation.project | Norges forskningsråd: 213115 | nb_NO |
dc.description.localcode | This is a Manuscript of an article published by Taylor & Francis in Journal of Experimental and Theoretical Artificial Intelligence on 04 Sep 2012, available online: http://www.tandfonline.com/doi/abs/10.1080/0952813X.2012.695448 | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 1 | |