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Machine Learning Based Workflows in Exploration and ProductionNormal access

Authors: J. Limbeck, M. Araya, G. Joosten, A. Eales, P. Gelderblom and D. Hohl
Event name: 79th EAGE Conference and Exhibition 2017 - Workshops
Session: WS01 Data Science for Geosciences
Publication date: 12 June 2017
DOI: 10.3997/2214-4609.201701656
Organisations: EAGE
Language: English
Info: Abstract, PDF ( 94.89Kb )
Price: € 20

Summary:
In this presentation we are going to cover a mapping between existing E&P workflow components and their data science based counterparts - as we have developed or envision them. We present one example from the geophysics domain, where deep neural nets are used to accelerate the seismic interpretation process (GeoDNN), and one example from the reservoir engineering domain (AutoSum) where machine learning is used to analyze are large ensemble of reservoir models.


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