Facies classification from well logs using an inception convolutional network

2017/06/02 by Valentin Tschannen, Matthias Delescluse, Tschannen, Valentin +5
Engineering · #86-04 #Computer Vision and Pattern Recognition (cs.CV) #Drilling and Well Engineering #FOS: Computer and information sciences #Hydraulic Fracturing and Reservoir Analysis #Hydrocarbon exploration and reservoir analysis

paper · pdf · doi:10.48550/arxiv.1706.00613

openalex publication_date 2017/06/02 · openalex created_date 2017/06/09 · openalex updated_date 2026/07/28

Abstract

The idea to use automated algorithms to determine geological facies from well logs is not new (see e.g Busch et al. (1987); Rabaute (1998)) but the recent and dramatic increase in research in the field of machine learning makes it a good time to revisit the topic. Following an exercise proposed by Dubois et al. (2007) and Hall (2016) we employ a modern type of deep convolutional network, called inception network (Szegedy et al., 2015), to tackle the supervised classification task and we discuss the methodological limits of such problem as well as further research opportunities.

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