Facies classification from well logs using an inception convolutional network
This work addresses facies classification for geologists, but it is incremental as it applies an existing deep learning method to a known problem.
The paper tackles the problem of geological facies classification from well logs by applying an inception convolutional network, achieving results that revisit and discuss methodological limits in this supervised task.
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 \textit{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.