Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles

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Article cité :

A New Optimization Model for 3d Well Design

E. M. E. M. Shokir, M. K. Emera, S. M. Eid, A. W. Wally

Oil & Gas Science and Technology - Rev. IFP, 59 3 (2004) 255-266

Publié en ligne : 2006-12-01

DOI: https://doi.org/10.2516/ogst:2004018

Citations de cet article :

The most accurate heuristic-based algorithms for estimating the oil formation volume factor

Mohammad Reza Mahdiani and Ghazal Kooti
Petroleum 2 (1) 40 (2016)
DOI: 10.1016/j.petlm.2015.12.001
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Two-phase Inflow Performance Relationship Prediction Using Two Artificial Intelligence Techniques: Multi-layer Perceptron Versus Genetic Programming

A. Sajedian, M. Ebrahimi and M. Jamialahmadi
Petroleum Science and Technology 30 (16) 1725 (2012)
DOI: 10.1080/10916466.2010.509074
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Compressibility factor model of sweet, sour, and condensate gases using genetic programming

Eissa M. El-M. Shokir, Musaed N. El-Awad, Adulhrahman A. Al-Quraishi and Osama A. Al-Mahdy
Chemical Engineering Research and Design 90 (6) 785 (2012)
DOI: 10.1016/j.cherd.2011.10.006
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A method for the optimization of well path in 3D heterogeneous reservoirs

Zehao Lyu, Xianzhi Song, Gensheng Li and Christopher Pain
Journal of Petroleum Science and Engineering (2018)
DOI: 10.1016/j.petrol.2018.09.040
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Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Kallol Biswas, Pandian M. Vasant, Moacyr Batholomeu Laruccia, José Antonio Gámez Vintaned and Myo M. Myint
Advances in Systems Analysis, Software Engineering, and High Performance Computing, Deep Learning Techniques and Optimization Strategies in Big Data Analytics 290 (2020)
DOI: 10.4018/978-1-7998-1192-3.ch017
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FSQGA based 3D complexity wellbore trajectory optimization

Linxiu Sha and Zhongqi Pan
Oil & Gas Sciences and Technology – Revue d’IFP Energies nouvelles 73 79 (2018)
DOI: 10.2516/ogst/2018008
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Multi-objective cellular particle swarm optimization for wellbore trajectory design

Jun Zheng, Chao Lu and Liang Gao
Applied Soft Computing 77 106 (2019)
DOI: 10.1016/j.asoc.2019.01.010
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