Scientists used machine learning to interpret the microscale heterogeneity of shale materials that influence water quality, based on their nanoscale properties.
Scientists have identified a way to use machine learning to integrate fine- and large-scale infrared characterizations of shale—sedimentary rocks composed of minerals and organic matter. The flow of fluids through shale’s nanoporous networks is fundamental to hydraulic fracturing and enhanced geothermal heating as well as to carbon sequestration and water storage. Thus, understanding shale chemistry at both the nano- and meso-scale is relevant to energy production, climate change mitigation, and sustainable water and land use.
The organic and mineralogical heterogeneity in shale at micrometer and nanometer spatial scales contributes to the quality of gas reserves, gas flow mechanisms and gas production. This work demonstrates two molecular imaging approaches based on infrared spectroscopy to obtain mineral and kerogen information at these mesoscale spatial resolutions in large-sized shale rock samples. The first method is a modified microscopic attenuated total reflectance measurement that utilizes a large germanium hemisphere combined with a focal plane array detector to rapidly capture chemical images of shale rock surfaces spanning hundreds of micrometers with micrometer spatial resolution. The second method, synchrotron infrared nano-spectroscopy, utilizes a metallic atomic force microscope tip to obtain chemical images of micrometer dimensions but with nanometer spatial resolution. This chemically “deconvoluted” imaging at the nano-pore scale is then used to build a machine learning model to generate a molecular distribution map across scales with a spatial span of 1000 times, which enables high-throughput geochemical characterization in greater details across the nano-pore and micro-grain scales and allows scientists to identify co-localization of mineral phases with chemically distinct organics and even with gas phase sorbents. This characterization is fundamental to understand mineral and organic compositions affecting the behavior of shales.
BER Program Manager
Zhao Hao, LBNL
Hao, Z., Bechtel, H. A., Kneafsey, T., Gilbert, B., and Nico, P. S. “Cross-Scale Molecular Analysis of Chemical Heterogeneity in Shale Rocks.” Scientific Reports 8, 2552 (2018). [DOI:10.1038/s41598-018-20365-6].
EESA Scientists Leverage Machine Learning
Scientists Use Machine Learning to Span Scales in Shale
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