Boosting NMR logging performance in oilfield applications: a robust ensemble machine learning framework

The borehole nuclear magnetic resonance technique is a technique widely used in exploration geophysics for subsurface structure imaging. It is primarily utilised in various projects including reservoir characterisation, hydrocarbon exploration, groundwater studies, and fracture characterisation. To interpret porosity, nuclear magnetic resonance (NMR) measurements are typically conducted alongside other logging methods such as caliper, resistivity, and gamma ray. However, due to cost and operational constraints, NMR data may not be acquired in all wells. Consequently, to predict this parameter in wells with missing data, the use of logs from other wells becomes necessary. Machine learning (ML) algorithms, increasingly prevalent, are well-suited for regression problems due to their capacity to model complex and latent relationships within data. Given the inherent difficulty in comprehending the intricate relationships within multi-dimensional spaces involving these measurement parameters, we implemented ML regression algorithms to map predictor parameters to response parameters. Thus, this study evaluates various ML regressors, including their ensemble learning counterparts, to compare their effectiveness in predicting NMR data, both individually and in combined configurations.