Machine Learning Assisted Identification of Hydrocarbon Storage and
Transport Pathways and Kerogen in Wolfcamp versus Barnett Shales
Abstract
A robust machine learning (ML) workflow was developed to identify
kerogen and hydrocarbon storage and transport pathways in the scanning
electron microscopy (SEM) maps of organic-rich shale samples from two
different formations, namely Wolfcamp Shale and Barnett Shale. Kerogen
is most often wrongly identified as pores/cracks are poor when the
machine learning model is developed on one shale formation and applied
on the other shale formation. ML workflow developed only on Barnett
shale cannot detect cracks in the Wolfcamp samples. The best performing
machine learning approach learnt from both the formations and exhibits
an average F1 scores of 0.99 and 0.91 on the inner-region and
outer-region pixels, respectively. The machine learning workflow
performs better on Barnett as compared to Wolfcamp. Barnett shale, in
comparison to Wolfcamp shale, provides better generalizable features and
more complex microstructural aspects that are harder to 2 identify.
Overall, it is easier to identify kerogen as compared to pores and
cracks due to their distribution, availability, connectivity, and pixel
intensity. By learning from merely 50 thousand pixels with corresponding
labels, the proposed machine learning workflow can successfully identify
the hydrocarbon storage and transport pathways and kerogen in a large
20-GB dataset containing approximately 10 billion pixels.