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Research Papers: Petroleum Engineering

Intelligent Classifier Approach for Prediction and Sensitivity Analysis of Differential Pipe Sticking: A Comparative Study

[+] Author and Article Information
Reza Jahanbakhshi

Young Researchers and Elites Club,
Science and Research Branch,
Islamic Azad University,
P.O. Box 14515-775,
Tehran 1477893855, Iran
e-mail: r.jahan62@gmail.com

Reza Keshavarzi

Young Researchers and Elites Club,
Science and Research Branch,
Islamic Azad University,
P.O. Box 14515-775,
Tehran 1477893855, Iran
e-mail: r.keshavarzi@gmail.com

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received March 8, 2014; final manuscript received February 4, 2016; published online March 10, 2016. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 138(5), 052904 (Mar 10, 2016) (10 pages) Paper No: JERT-14-1070; doi: 10.1115/1.4032831 History: Received March 08, 2014; Revised February 04, 2016

Prediction of differential pipe sticking (DPS) prior to occurrence, and taking preventive measures, is one of the best approaches to minimize the risk of DPS. In this paper, probabilistic artificial neural network (ANN) has been introduced. Moreover, conventional ANNs through multilayer perceptron (MLP) and radial basis function (RBF) have been used to compare with probabilistic ANN. Furthermore, to determine the most important parameters, forward selection sensitivity analysis has been applied. By predicting DPS and performing sensitivity analysis, it is possible to improve well planning process. The results from the analyses have shown the better potentiality of the probabilistic ANN in this area.

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References

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Figures

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Fig. 2

Atypical structure of the ANN

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Fig. 3

Typical single neuron and its transfer function [17]

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Fig. 4

Transfer functions [17]

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Fig. 5

Feedforward neural network using backpropagation algorithm [17]

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Fig. 6

RBF neural network [17]

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Fig. 7

Probabilistic neural network [17]

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Fig. 8

Comparison ANNs prediction and actual condition for testing subset

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