Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning-Reference-Cited by-同舟云学术

Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning

Published:2020-09-22 Issue:1 Volume:25 Page:285-297
ISSN:1420-0597
Container-title:Computational Geosciences
language:en
Short-container-title:Comput Geosci

Author:

Bai Tao,Tahmasebi Pejman^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computational Theory and Mathematics,Computers in Earth Sciences,Computer Science Applications

Link

https://link.springer.com/content/pdf/10.1007/s10596-020-10005-2.pdf

Reference49 articles.

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3. Wagenhofer, T., Hatzignatiou, D.G.: Optimization of horizontal well placement. In: SPE Western Regional Meeting. Society of Petroleum Engineers (1996)

4. Muskat, M., Wycokoff, R.D.: An approximate theory of water-coning in oil production. Trans. AIME. 114, 144–163 (1935). https://doi.org/10.2118/935144-g

5. Chaney, P.E., Noble, M.D., Henson, W.L., Rice, T.D.: How to perforate your well to prevent water and gas coning. Oil Gas J. 55, 108–114 (1956)

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