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

Title

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

Author

Stroet, C.T.
Zwaan, J.
de Jager, G.
Montijn, R.
Schuren, F.

Publication year

2017

Subject

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

To reference this document use:

http://resolver.tudelft.nl/uuid:209df43d-3b7a-41aa-81db-b5815b649577

DOI

https://doi.org/10.15530/urtec-2017-2671117

TNO identifier

842151

Publisher

Unconventional Resources Technology Conference (URTEC)

ISBN

9781613995433

Source

SPE/AAPG/SEG Unconventional Resources Technology Conference 2017. 24 July 2017 through 26 July 2017, 126

Article number

2671117

Document type

conference paper