Experimental measurement and modeling of water-based drilling mud density using adaptive boosting decision tree, support vector machine, and K-nearest neighbors: A case study from the South Pars gas field-Reference-Cited by-同舟云学术

Experimental measurement and modeling of water-based drilling mud density using adaptive boosting decision tree, support vector machine, and K-nearest neighbors: A case study from the South Pars gas field

Published:2021-12 Issue: Volume:207 Page:109132
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Hashemizadeh Abbas^ORCID,Maaref Ahmad,Shateri Mohammadhadi^ORCID,Larestani Aydin,Hemmati-Sarapardeh Abdolhossein^ORCID

Funder

Academy of Neonatal Nursing

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference39 articles.

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3. Toward reliable model for prediction drilling fluid density at wellbore conditions: a LSSVM model;Ahmadi;Neurocomputing,2016

4. An accurate model to predict drilling fluid density at wellbore conditions;Ahmadi;Egypt. J. Pet.,2018

5. An introduction to kernel and nearest-neighbor nonparametric regression;Altman;Am. Statistician,1992

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