Prediction of stratigraphic units from spectral co-occurance coefficients of well logs

TitlePrediction of stratigraphic units from spectral co-occurance coefficients of well logs
Publication TypeConference
Year of Publication2015
AuthorsBen B. Bougher, Felix J. Herrmann
Conference NameCSEG Annual Conference Proceedings
Month05
KeywordsCSEG, machine learning, scattering transform, well logs
Abstract

Well logging is the process of making physical measurements down bore holes in order to characterize geological and structural properties. Logs are visually interpreted and correlated to classify regions that are similar in structure, a process that can be modelled with machine learning. This project applies supervised learning methods to labelled well logs from the Trenton Black River data set in order to classify major stratigraphic units. Spectral co-occurance coefficients were used for feature extraction, and a k-nearest-neighbours approach was used for classification. This novel approach was applied to real field data in a high-impact domain, yielding promising results for future research.

Notes

(CSEG, Calgary)

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Conferences/CSEG/2015/bougher2015CSEGpsu/bougher2015CSEGpsu.pdf
Presentation

https://www.slim.eos.ubc.ca/Publications/Public/Conferences/CSEG/2015/bo...

Citation Keybougher2015CSEGpsu