Accelerating Seismic Reservoir Characterization with Rock Physics-driven Machine Learning

Seismic reservoir characterization aims to integrate various geophysical datasets to predict better subsurface models. Usually, seismic inversion workflow integrates wells and seismic data together to predict elastic models of the subsurface. However, elastic models are further interpreted into reservoir properties using traditional multi-linear regression or neural networks thereby increasing turnaround time and reducing precision as these reservoir properties are predicted one by one.

The latest integration of rock physics with machine learning enables geoscientists to use convolutional neural network (CNN) in reservoir characterization. This allows for the simultaneous prediction of both elastic and reservoir properties directly from seismic gather data. Integrating rock physics into the workflow is crucial as it allows us to simulate various geological scenarios in the subsurface. By doing this, we can predict the corresponding elastic properties, which in turn allows for the effective use of deep learning in reservoir characterization.