AI-aided core analysis: Faster and cheaper SCAL studies

A. Erofeev, D. Orlov, Dmitry Koroteev

    Результат исследований: Вклад в конференциюДокументрецензирование

    1 Цитирования (Scopus)

    Аннотация

    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.

    Язык оригиналаАнглийский
    СостояниеОпубликовано - 2019
    СобытиеProfessional Geological Research and Exploration Scientific Seminar 2019, ProGREss 2019 - Sochi, Российская Федерация
    Продолжительность: 5 нояб. 20198 нояб. 2019

    Конференция

    КонференцияProfessional Geological Research and Exploration Scientific Seminar 2019, ProGREss 2019
    Страна/TерриторияРоссийская Федерация
    ГородSochi
    Период5/11/198/11/19

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