Carbonate / siliciclastic lithofacies classification aided by well-log derivative, volatility and sequence boundary attributes combined with machine learning-Reference-Cited by-同舟云学术

Carbonate / siliciclastic lithofacies classification aided by well-log derivative, volatility and sequence boundary attributes combined with machine learning

Published:2022-06-10 Issue:3 Volume:15 Page:1699-1721
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Wood David A.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

https://link.springer.com/content/pdf/10.1007/s12145-022-00829-0.pdf

Reference56 articles.

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2. Agrawal R, Malik A, Samuel R, Saxena A (2022) Real-Time Prediction of litho-facies from drilling data using an artificial neural network: a comparative field data study with optimizing algorithms. J Energy Res Technol 144:043003. https://doi.org/10.1115/1.4051573

3. Al-Mudhafar WJ (2017) Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms. J Petrol Explor Prod Technol 7:1023–1033. https://doi.org/10.1007/s13202-017-0360-0

4. Al-Mudhafar WJ, Al Lawe EM, Noshi CI (2019) Clustering analysis for improved characterization of carbonate reservoirs in a Southern Iraqi oil field. Offshore Technology Conference, Houston, Texas, U.S.A. May 2019. https://doi.org/10.4043/29269-MS

5. Al-Mudhafar WJ (2020). Advanced supervised machine learning algorithms for efficient electrofacies classification of a carbonate reservoir in a giant Southern Iraqi oil field. Offshore Technology Conference, Houston, Texas, U.S.A. May 2020. https://doi.org/10.4043/30906-MS

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