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Energy Sources, Part A: Recovery, Utilization, and Environmental Effects Volume 34, 2012 - Issue 7
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Original Articles

Investigation of the Most Efficient Approach of the Prediction of the Rate of Penetration

R. Arabjamaloei Department of Petroleum Engineering, Islamic Azad University, Omidieh Branch, Omidieh, Iran
&
B. Karimi Dehkordi Iranian Central Oil Fields Company, Tehran, Iran
Pages 581-590 | Received 17 Apr 2010, Accepted 13 May 2010, Published online: 24 Feb 2012

Citations (11)

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Xinxin Fang, Hong Feng & Hao Wang. (2022) Study on intelligent prediction method of rock drillability based on Bayesian lithology classification and optimized BP neural network. Petroleum Science and Technology 40:17, pages 2141-2162.
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Articles from other publishers (10)

Chinedu I. Ossai & Ugochukwu I. Duru. (2022) Applications and theoretical perspectives of artificial intelligence in the rate of penetration. Petroleum 8:2, pages 237-251.
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Naipeng Liu, Hui Gao, Zhen Zhao, Yule Hu & Longchen Duan. (2021) A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: a case study in Xinjiang. Journal of Petroleum Exploration and Production Technology 12:6, pages 1595-1608.
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Abiodun Ismail Lawal, Sangki Kwon & Moshood Onifade. (2021) Prediction of rock penetration rate using a novel antlion optimized ANN and statistical modelling. Journal of African Earth Sciences 182, pages 104287.
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Mohammad Mehrad, Mahdi Bajolvand, Ahmad Ramezanzadeh & Jalil Ghavidel Neycharan. (2020) Developing a new rigorous drilling rate prediction model using a machine learning technique. Journal of Petroleum Science and Engineering 192, pages 107338.
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Abdolhossein Hemmati-Sarapardeh, Aydin Larestani, Menad Nait Amar & Sassan Hajirezaie. 2020. Applications of Artificial Intelligence Techniques in the Petroleum Industry. Applications of Artificial Intelligence Techniques in the Petroleum Industry 229 278 .
Luís Felipe F.M. Barbosa, Andreas Nascimento, Mauro Hugo Mathias & João Andrade de CarvalhoJr.Jr.. (2019) Machine learning methods applied to drilling rate of penetration prediction and optimization - A review. Journal of Petroleum Science and Engineering 183, pages 106332.
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Reza Mikaeil, Sina Shaffiee Haghshenas & Seyed Hadi Hoseinie. (2017) Rock Penetrability Classification Using Artificial Bee Colony (ABC) Algorithm and Self-Organizing Map. Geotechnical and Geological Engineering.
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Reza Asgharzadeh Shishavan, Casey Hubbell, Hector D. Perez, John D. Hedengren, David S. Pixton & Anthony P. Pink. (2016) Multivariate Control for Managed-Pressure-Drilling Systems by Use of High-Speed Telemetry. SPE Journal 21:02, pages 459-470.
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Manoj Khandelwal & Danial Jahed Armaghani. (2015) Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique. Geotechnical and Geological Engineering 34:2, pages 605-620.
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H. Basarir, L. Tutluoglu & C. Karpuz. (2014) Penetration rate prediction for diamond bit drilling by adaptive neuro-fuzzy inference system and multiple regressions. Engineering Geology 173, pages 1-9.
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