Unsupervised and Physics-Informed Machine Learning of Big and Noisy Data
V.V. Vesselinov
Bureau of Economic Geology, University of Austin, Texas2020
Summary
This presentation covers unsupervised and physics-informed machine learning analyses for characterizing energy production from unconventional reservoirs. It discusses the integration of geological, geochemical, and geophysical data with machine learning techniques to identify hidden patterns and assess resource potential.