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Predicting sweet spots in shale plays by DNA fingerprinting and machine learning

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Author: Stroet, C.T. · Zwaan, J. · Jager, G. de · Montijn, R. · Schuren, F.
Type:article
Date:2017
Publisher: Unconventional Resources Technology Conference (URTEC)
Source:SPE/AAPG/SEG Unconventional Resources Technology Conference 2017. 24 July 2017 through 26 July 2017, 126
Identifier: 842151
doi: doi:10.15530/urtec-2017-2671117
Article number: 2671117
Keywords: Artificial intelligence · Bacteria · DNA · Hydrocarbons · Infill drilling · Iterative methods · Learning systems · Oil shale · Resource valuation · Seepage · Shale · Soil surveys · Soils · Complex compositions · DNA fingerprinting · Haynesville shales · Hydrocarbon accumulation · Hydrocarbon-oxidizing bacteria · Machine learning applications · Microbial species · Oil and gas prices · Big data