Advantages of Machine Learning over Seismic Inversion for Reservoir Characterization | ENGLISH

Check out more on the GeoInsights website: https://bit.ly/3ywZPXb Alvaro Chaveste and Rocky Roden presented a new Machine Learning (ML) methodology for Reservoir Characterization that is done at a fraction of the time compared to seismic inversion. The ML methodology is based on computing Self Organized Maps (SOM), an unsupervised form of ML, and cross-referencing the ML output to lithofacies from petrophysical logs. The methodology, tested using synthetic seismic data and applied to the U.S. Niobrara formation, defines the lithofacies of interest at high resolution. See more about Reservoir Characterization on the Geophysical Insights website: https://bit.ly/4cGwacw #reservoir #som #petrochemical #niobrara #machinelearning #geophysicalinsights #lithofacies #thoughtflow #seismic