AI-aided core analysis: Faster and cheaper SCAL studies

A. Erofeev, D. Orlov, Dmitry Koroteev

    Research output: Contribution to conferencePaperpeer-review

    1 Citation (Scopus)

    Abstract

    The main aim of this work is to study the applicability of Machine Learning (ML) techniques for prediction ofrock properties, which are commonly defined via special core analysis (SCAL). The mechanism of SCALprediction on the basis of routine core analysis (RCA) was developed and validated. The possibility ofapplication of ML methods for estimation of some rock characteristics was demonstrated. The comparativeanalysis of different ML techniques was provided to choose the most stable and accurate forecast methods. Itwas shown that Gradient Boosting algorithm and Artificial Neural Network allow to create the most robust andaccurate models for considered rock properties.

    Original languageEnglish
    Publication statusPublished - 2019
    EventProfessional Geological Research and Exploration Scientific Seminar 2019, ProGREss 2019 - Sochi, Russian Federation
    Duration: 5 Nov 20198 Nov 2019

    Conference

    ConferenceProfessional Geological Research and Exploration Scientific Seminar 2019, ProGREss 2019
    Country/TerritoryRussian Federation
    CitySochi
    Period5/11/198/11/19

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