LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Quantitative Image Analysis of Source Rocks Using Machine Learning Segmentation

Photo from wikipedia

Source rocks are composed of inorganic minerals, clay particles, and organic matter that are compacted to create a composite with texture and pore sizes that vary at the nanoand micro-scales… Click to show full abstract

Source rocks are composed of inorganic minerals, clay particles, and organic matter that are compacted to create a composite with texture and pore sizes that vary at the nanoand micro-scales [1-2]. These rocks exhibit chemical variations due to the dominant framework phases ( e.g. calcite, dolomite, or quartz) and the clay minerals (e.g. illite, smectite, or kaolinite), while exhibiting variations in the organic components (kerogen, bitumen, and/or pyrobitumen) where the relative amount and molecular composition varies with thermal maturity [2-4]. Scanning Electron Microscopy (SEM) is commonly used for source rock characterization to quantify the fraction of various components, porosity, and pore size distribution, which are used to evaluate reservoir quality during hydrocarbon exploration. This variation in structural and chemical heterogeneity as well as imaging artifacts such as charging, surface contamination, and surface damage from sample preparation, create images with a broad multi-modal intensity histogram that is challenging to accurately segment [5].

Keywords: microscopy; quantitative image; source; image analysis; analysis source; source rocks

Journal Title: Microscopy and Microanalysis
Year Published: 2020

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.