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

conference paper




Stroet, C.T.

Zwaan, J.

Jager, G. de

Montijn, R.

Schuren, F.
Topics





















Artificial intelligenceBacteriaDNAHydrocarbonsInfill drillingIterative methodsLearning systemsOil shaleResource valuationSeepageShaleSoil surveysSoilsComplex compositionsDNA fingerprintingHaynesville shalesHydrocarbon accumulationHydrocarbon-oxidizing bacteriaMachine learning applicationsMicrobial speciesOil and gas pricesBig data
TNO Identifier
842151
Repository link
https://resolver.tno.nl/uuid:209df43d-3b7a-41aa-81db-b5815b649577
DOI
https://dx.doi.org/10.15530/urtec-2017-2671117
ISBN
9781613995433
Publisher
Unconventional Resources Technology Conference (URTEC)
Article nr.
2671117
Source title
SPE/AAPG/SEG Unconventional Resources Technology Conference 2017. 24 July 2017 through 26 July 2017
Pages
126
Files
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