Standard

Interpretation of seismic inversion results using the “Random Forest”. / Butorin, A. V.

Data Science in Oil and Gas 2020. European Association of Geoscientists and Engineers, 2020. (Data Science in Oil and Gas 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Butorin, AV 2020, Interpretation of seismic inversion results using the “Random Forest”. in Data Science in Oil and Gas 2020. Data Science in Oil and Gas 2020, European Association of Geoscientists and Engineers, 1st Regional Conference on Data Science in Oil and Gas 2020, Virtual, Online, 19/10/20. https://doi.org/10.3997/2214-4609.202054018

APA

Butorin, A. V. (2020). Interpretation of seismic inversion results using the “Random Forest”. In Data Science in Oil and Gas 2020 (Data Science in Oil and Gas 2020). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202054018

Vancouver

Butorin AV. Interpretation of seismic inversion results using the “Random Forest”. In Data Science in Oil and Gas 2020. European Association of Geoscientists and Engineers. 2020. (Data Science in Oil and Gas 2020). https://doi.org/10.3997/2214-4609.202054018

Author

Butorin, A. V. / Interpretation of seismic inversion results using the “Random Forest”. Data Science in Oil and Gas 2020. European Association of Geoscientists and Engineers, 2020. (Data Science in Oil and Gas 2020).

BibTeX

@inproceedings{8a82abfc3a144177a31a1479a033d908,
title = "Interpretation of seismic inversion results using the “Random Forest”",
abstract = "The study is aimed to estimate the possibility of using machine-learning method “Random Forest”, to obtain a probabilistic estimate of the distribution of an oil-saturated reservoir. The object of research is the Achimov complex, composed of relatively thin interlayered terrigenous rocks. The “random forest” method realized with the scikit-learn Python library of the. Application of the algorithm converts the input cubes of elastic parameters to probability cubes of lithotypes, which used for geological interpretation. As a result, the trends of reservoir properties estimated, as well as the probability cube of an oil-saturated reservoir. These data can be effectively used in planning the well-paths.",
author = "Butorin, {A. V.}",
note = "Publisher Copyright: {\textcopyright} 2021 EAGE Publications BV.; null ; Conference date: 19-10-2020 Through 20-10-2020",
year = "2020",
doi = "10.3997/2214-4609.202054018",
language = "English",
series = "Data Science in Oil and Gas 2020",
publisher = "European Association of Geoscientists and Engineers",
booktitle = "Data Science in Oil and Gas 2020",
address = "Netherlands",

}

RIS

TY - GEN

T1 - Interpretation of seismic inversion results using the “Random Forest”

AU - Butorin, A. V.

N1 - Publisher Copyright: © 2021 EAGE Publications BV.

PY - 2020

Y1 - 2020

N2 - The study is aimed to estimate the possibility of using machine-learning method “Random Forest”, to obtain a probabilistic estimate of the distribution of an oil-saturated reservoir. The object of research is the Achimov complex, composed of relatively thin interlayered terrigenous rocks. The “random forest” method realized with the scikit-learn Python library of the. Application of the algorithm converts the input cubes of elastic parameters to probability cubes of lithotypes, which used for geological interpretation. As a result, the trends of reservoir properties estimated, as well as the probability cube of an oil-saturated reservoir. These data can be effectively used in planning the well-paths.

AB - The study is aimed to estimate the possibility of using machine-learning method “Random Forest”, to obtain a probabilistic estimate of the distribution of an oil-saturated reservoir. The object of research is the Achimov complex, composed of relatively thin interlayered terrigenous rocks. The “random forest” method realized with the scikit-learn Python library of the. Application of the algorithm converts the input cubes of elastic parameters to probability cubes of lithotypes, which used for geological interpretation. As a result, the trends of reservoir properties estimated, as well as the probability cube of an oil-saturated reservoir. These data can be effectively used in planning the well-paths.

UR - http://www.scopus.com/inward/record.url?scp=85099725807&partnerID=8YFLogxK

U2 - 10.3997/2214-4609.202054018

DO - 10.3997/2214-4609.202054018

M3 - Conference contribution

AN - SCOPUS:85099725807

T3 - Data Science in Oil and Gas 2020

BT - Data Science in Oil and Gas 2020

PB - European Association of Geoscientists and Engineers

Y2 - 19 October 2020 through 20 October 2020

ER -

ID: 88694733