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.
ASJC Scopus subject areas
- Computer Science(all)