A comparison of machine learning surrogate models for net present value prediction from well placement binary data-Reference-Cited by-同舟云学术

A comparison of machine learning surrogate models for net present value prediction from well placement binary data

Published:2022-01 Issue: Volume:208 Page:109208
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Bertini João Roberto^ORCID,Ferreira Batista Sérgio,Funcia Mei Abe,Mendes da Silva Luis Otávio^ORCID,Santos Antonio Alberto S.^ORCID,Schiozer Denis José

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference40 articles.

1. Machine learning for combinatorial optimization: A methodological tour d’horizon;Bengio;European J. Oper. Res.,2021

2. A comparison of machine learning algorithms as surrogate model for net present value prediction from wells arrangement data;Bertini Jr.,2019

3. Advances in surrogate based modeling, feasibility analysis, and optimization: A review;Bhosekar;Comput. Chem. Eng.,2018

4. Use of well indicators in the production strategy optimization process;Botechia,2013

5. A mean–variance objective for robust production optimization in uncertain geological scenarios;Capolei;J. Pet. Sci. Eng.,2015

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