Figure 2
From: A machine learning framework for rapid forecasting and history matching in unconventional reservoirs
A magnified look at the steps to produce an ML inverse model illustrating how the transfer learning paradigm uses both synthetic data from reduced-order models as well as high-fidelity models. Note that in this workflow, we used Patzek model as the reduced-order model, but the same workflow can be used with other reduced-order model choices that may be physics-based or even data-driven.
