Robotized petrophysics: Machine learning and thermal profiling for automated mapping of lithotypes in unconventionals

Yury Meshalkin, Dmitry Koroteev, Evgeniy Popov, Evgeny Chekhonin, Yury Popov

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)

    Abstract

    We present a method for predicting rock types. The method is based on continuous high-resolution thermal logging along full-size core samples and being applied for rocks from a major unconventional formation. The method utilizes spatial spectral decomposition and machine learning approaches allowing automatic classification of the core samples over lithological groups within an isolated stratigraphic depth interval of a wellbore. The core samples are basically classified to the particular lithotypes by means of spectral representation of profiles of thermal properties obtained by a modern contactless method.

    Original languageEnglish
    Pages (from-to)944-948
    Number of pages5
    JournalJournal of Petroleum Science and Engineering
    Volume167
    DOIs
    Publication statusPublished - Aug 2018

    Keywords

    • Bazhenov formation
    • Geostatistics
    • Lithotype
    • Machine learning
    • Thermal properties of core sample

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