Capturing Uncertainty in Machine Learning for Geoscience Applications

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Presenter: Ehsan Naeini, PhD, Chief Product Officer, Earth Science Analytics AS

Abstract: In quantitative reservoir characterization workflows, it is common to incorporate the uncertainty of predictions thus such subsurface models should provide calibrated probabilities and the associated uncertainties in their predictions. Whilst Machine Learning is being utilised or tested at different geoscience application domains, the uncertainty associated with their prediction is often ignored. We introduce and compare different approaches to obtaining probabilistic ML models and show different case studies for well data and seismic based applications. Overall, we observe that the resulting uncertainties offer a possibility to consider different scenarios in subsurface modeling and further improve the model performance as well as enhancing the interpretability of the models.

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