Facimage: A Machine Learning-based Approach for Faster and More Precise Rock Type Classification
Presented by: Kim McLean, Team Lead - Formation Evaluation
Featured technology: Geolog™ Facimage
Well log data provides a wealth of information about the subsurface, from mineralogy to porosity, permeability and saturation. In this presentation, we will look at ways to leverage our wireline and petrophysical data using an advanced electrofacies toolkit complete with clustering and neural network routines for electrofacies analysis. We’ll look at facies identification in non-cored intervals to prediction of petrophysical properties using Multi-Resolution Graph-based Clustering (MRGC).
Kim McLean, Team Lead - Formation Evaluation
Kim McLean received her Bachelors of Science degree from the University of New Orleans before continuing to Central Washington University, where she studied the structural geology of the Tien Shan in Kyrgystan for her Master’s thesis. After receiving her MSc, Kim entered the energy industry, and now has over 15 years of experience. She worked at Halliburton and Paradigm before spending two years as the petrophysicist for the Pike Asset team with BP in Calgary. At Emerson E&P Software, Kim applies her practical petrophysical experience to the work she does with the Geolog Formation Evaluation application.