@article{53d6fe5bbe09450ba1b2a7631c5fa007,
title = "Multivariate imputation via chained equations for elastic well log imputation and prediction",
abstract = "Well logging is essential in studies which require an understanding of the subsurface geology and depositional conditions. Unfortunately, the measurements are rarely complete and missing data intervals are common due to operational issues or malfunction of the logging equipment. Therefore the imputation of missing data from down-hole well logs is a common problem in subsurface workflows. Recently, many different approaches have been used for imputation but they are often manual or data set specific. Machine learning has reignited interest in this field with promises of a more generic and simpler approach. We explore whether the chaining of machine learning for mutli-log imputation improves results by overcoming disparities in the patterns of missing data. Our research interest is primarily petroleum geophysics and therefore this study focuses on the elastic logs of compressional (DT) and shear (DTS) sonic along with the bulk density (RHOB). However, the method may be applied to all sufficiently large well log data sets in any industry.",
author = "Antony Hallam and Debajoy Mukherjee and Romain Chassagne",
note = "Funding Information: We would like to thank the sponsors of the Edinburgh Time-Lapse Project, Phase VII: AkerBP , BP , CGG , Chevron/Ithaca Energy, CNOOC , Equinor , ConocoPhillips, ENI , Petrobras, Norsar , Woodside, Taqa , Halliburton , ExxonMobil, OMV and Shell for financial support. Equinor for provision of the Volve public data set. The FORCE 2020 sponsors and the Norwegian Government for the F2020 data set. Thank you to colleagues and peers for their help and feedback. We also acknowledge Schlumberger for the use of their software and the Python open source community. Funding Information: We would like to thank the sponsors of the Edinburgh Time-Lapse Project, Phase VII: AkerBP, BP, CGG, Chevron/Ithaca Energy, CNOOC, Equinor, ConocoPhillips, ENI, Petrobras, Norsar, Woodside, Taqa, Halliburton, ExxonMobil, OMV and Shell for financial support. Equinor for provision of the Volve public data set. The FORCE 2020 sponsors and the Norwegian Government for the F2020 data set. Thank you to colleagues and peers for their help and feedback. We also acknowledge Schlumberger for the use of their software and the Python open source community. We would also like to thank the reviewers of this paper, who provided invaluable feedback and advice during the publication process. Publisher Copyright: {\textcopyright} 2022 The Authors",
year = "2022",
month = jun,
doi = "10.1016/j.acags.2022.100083",
language = "English",
volume = "14",
journal = "Applied Computing and Geosciences",
issn = "2590-1974",
publisher = "International Association for Mathematical Geosciences",
}