Machine learning applications for pore space composition
Julian is fascinated with machine learning, after completing an internship with Bluware in the Summer of 2018. In his first application at OU, he is employing a principal component analysis (PCA) method, applied to a selected set of seismic attributes. This processes aids in identifying a meaningful combination of attributes that may allow us to better understand and map hydrocarbons in the subsurface. Initial projects will investigate methane and undersaturated gas accumulations. Julian has presented these results at San Antonio’s AAPG May 2019 conference, as well as the Future Leader’s Forum of the World Petroleum Council 2019, where he won a second place award for his presentation.
For the 2019-2020 academic year, Julian is serving as the president of OU’s SEG Student Chapter.
Teamed with Karelia La Marca, Julian and Karelia came in fifth place at the SEG International Challenge Bowl at SEG 2019.
LinkedIn: Julian Chenin
