Machine learning for geophysical characterization of brittleness

Mark Mlella, a former MS. graduate student working with Dr. Rui Zhang, has recently published his thesis work on the journal Interpretation. 
The paper introduced a machine learning technique to estimate the brittleness of the Tuscaloosa Marine Shale from the seismic datasets, which could benefit the unconventional resource development around southern Louisiana.

This work is funded by the Department of Energy, National Energy Technology Laboratory under award number DE-FE0031575 (the Tuscaloosa Marine Shale Laboratory) in collaboration with Petroleum Engineering Department.

Mark Mlella is currently pursuing his PhD in the University of Alberta, CA.