Citations in impact factor journals
Drilling cost optimization in a hydrocarbon field by combination of comparative and mathematical methods Petroleum Science Vol. 6, Issue 4, 451, 2009 |
Machine learning methods applied to drilling rate of penetration
prediction and optimization - A review Journal of Petroleum Science and Engineering |
Is Support Vector Regression method suitable for predicting rate of penetration? Journal of Petroleum Science and Engineering |
Penetration rate prediction in heterogeneous formations: A geomechanical approach through machine learning Journal of Petroleum Science and Engineering |
Computational prediction of the drilling rate of penetration (ROP): A comparison of various machine learning approaches and traditional models Journal of Petroleum Science and Engineering |
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Drilling rate of penetration prediction through committee support
vector regression based on imperialist competitive algorithm Carbonates and Evaporites Vol. 32, Issue 2, 205, 2017 |
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How to cite this article
M.H. Bahari, A. Bahari, F. Nejati Moharrami and M.B. Naghibi Sistani, 2008. Determining Bourgoyne and Young Model Coefficients Using Genetic Algorithm to Predict Drilling Rate. Journal of Applied Sciences, 8: 3050-3054.
DOI: 10.3923/jas.2008.3050.3054
URL: https://scialert.net/abstract/?doi=jas.2008.3050.3054
DOI: 10.3923/jas.2008.3050.3054
URL: https://scialert.net/abstract/?doi=jas.2008.3050.3054