Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field-Reference-Cited by-同舟云学术

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Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field

Published:2019-04 Issue: Volume:175 Page:604-623
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Ashrafi Seyed Babak,Anemangely Mohammad,Sabah Mohammad,Ameri Mohammad Javad

Funder

National Iranian oil Company

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference98 articles.

1. A comprehensive review of swarm optimization algorithms;Ab Wahab;PLoS One,2015

2. Drilling rate at the technical limit;Akgun;Int. J. Petrol. Sci. Technol.,2007

3. Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: a case study from Ab-Teymour Oilfield;Anemangely;J. Nat. Gas Sci. Eng.,2017

4. Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network;Anemangely;J. Geophys. Eng.,2018

5. Drilling rate of penetration prediction through committee support vector regression based on imperialist competitive algorithm;Ansari;Carbonates Evaporites,2017

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