Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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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