In recent years, machine learning (ML) approaches have gained significant attention in subsurface property estimation problems. However, due to the data-driven nature, people often interrogate the reliability of such ML approaches, especially for regions where ground truth data are sparse. In this study, in the application of subsurface property estimation, we investigate the feasibility to evaluate the goodness-of-fit of ML predictions by using a backward ML model. We find that such backward ML models provides an alternative way to qualitatively evaluate the ML prediction results in case ground truth data are not available.
Keywords:log analysis, upstream oil & gas, ground truth property, ml prediction, well logging, relation, forward ml model, exploration geophysicist 10, machine learning, artificial intelligence
This content is only available via PDF.
Copyright 2021, Society of Exploration Geophysicists