MSRL Visualizes Core Data Using Innovative CorePy Program

July 2, 2020
Core characterization

The Bureau’s Mudrock Systems Research Lab (MSRL) team is finding new ways of visualizing the geochemical and geomechanical measurements taken from the core they study: through Python automation. The team of Toti Larson, Esben Pedersen, Priyanka Periwal, and Eval Sivil have created CorePy, a Python tool that uses principal component analysis and K-means clustering to integrate data analytics with graphical visualization. With CorePy, the team can view the statistical distribution of important rock attributes directly and in real time. The core dataset for CorePy spans collections from the Eagle Ford Shale, Permian Basin, Vaca Muerta Formation, Haynesville, Barnett, and other mudrock plays.

"High-resolution X-ray fluorescence (XRF) is a powerful geochemical tool used to characterize geological core, but geologists have struggled to fully utilize the large multivariate datasets that it generates," said Toti Larson, who leads the research team in the MSRL that developed CorePy. "The application allows researchers to integrate these complex datasets and visualize the results directly on core photographs, and to apply new data analysis tools, quickly visualize the results, and better characterize the core."