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AAPG Bulletin, Preliminary version published online Ahead of Print 1 October 2022.

Copyright © 2022. The American Association of Petroleum Geologists. All rights reserved.


Machine learning classification of Austin Chalk chemofacies from high-resolution X-ray fluorescence core characterization

Toti E. Larson, Robert G. Loucks, J. Evan Sivil, Kelly E. Hattori, and Christopher K. Zahm

Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX

Ahead of Print Abstract

The Late Cretaceous Austin Chalk Group is an unconventional reservoir that extends across Texas and Louisiana. It is composed of interbedded layers of marly chalks to calcareous-siliciclastic mudrocks that vary in degree of lamination, bioturbation, mineral abundance, and organic-matter richness. Integrating lithologic observations with geochemistry is critical for interpreting depositional environments and modelling reservoir properties. Central to this integration is the ability to characterize the geochemistry of core samples at a resolution that captures thin-layered heterogeneity common to mudrock systems. Here we develop a training dataset using a semi-supervised chemofacies clustering approach that is explored with a deep neural network model to predict chemofacies across multiple cores of the Austin Chalk Group. Eight chemofacies are identified that capture differences in inorganic geochemistry, mineral abundance, rock fabric, and organic-matter richness; three classify differences in the marly chalks, four classify differences in the calcareous-siliciclastic mudrocks, and one is transitional between marly chalk and calcareous-siliciclastic mudrocks. Two distinct siliciclastic-carbonate mixing trends are identified that differ in modal abundances of tectosilicates and total clay. Two chemofacies are distinguished based on differences in molybdenum and vanadium trace element enrichment, suggesting differences in bottom water redox chemistry. Collectively, this approach provides a means to integrate geochemical measurements and lithological observations to interpret depositional environments of mudrock systems, and is an important step towards upscaling core data to characterize reservoir quality.

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Please cite this AAPG Bulletin Ahead of Print article as:

Toti E. Larson, Robert G. Loucks, J. Evan Sivil, Kelly E. Hattori, Christopher K. Zahm: Machine learning classification of Austin Chalk chemofacies from high-resolution X-ray fluorescence core characterization, (in press; preliminary version published online Ahead of Print 01 October 2022: AAPG Bulletin, DOI:10.1306/09232220095.