Machine learning methods applied to drilling rate of penetration prediction and optimization - A review-Reference-Cited by-同舟云学术

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Machine learning methods applied to drilling rate of penetration prediction and optimization - A review

Published:2019-12 Issue: Volume:183 Page:106332
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Barbosa Luís Felipe F.M.,Nascimento Andreas,Mathias Mauro Hugo,de Carvalho João Andrade

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference199 articles.

1. Modeling rate of penetration for deviated wells using artificial neural network;Abbas,2018

2. Outlier Analysis;Aggarwal,2013

3. Artificial intelligence techniques and their applications in drilling fluid engineering: a review;Agwu;J. Pet. Sci. Eng.,2018

4. Toward reliable model for prediction Drilling Fluid Density at wellbore conditions: a LSSVM model;Ahmadi;Neurocomputing,2016

5. Computational intelligence based prediction of drilling rate of penetration: a comparative study;Ahmed;J. Pet. Sci. Eng.,2019

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