Technical Brief

A New Support Vector Machine and Artificial Neural Networks for Prediction of Stuck Pipe in Drilling of Oil Fields

[+] Author and Article Information
Habib Rostami

Computer Engineering Department,
School of Engineering,
Persian Gulf University,
Bushehr 7516913817, Iran
e-mail: habib@pgu.ac.ir

Abbas Khaksar Manshad

Department of Petroleum Engineering,
Abadan Faculty of Petroleum Engineering,
Petroleum University of Technology,
Abadan 6314661118, Iran
e-mail: khaksar@pgu.ac.ir

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received July 16, 2013; final manuscript received January 28, 2014; published online April 9, 2014. Assoc. Editor: Christopher J. Wajnikonis.

J. Energy Resour. Technol 136(2), 024502 (Apr 09, 2014) (4 pages) Paper No: JERT-13-1210; doi: 10.1115/1.4026917 History: Received July 16, 2013; Revised January 28, 2014

Stuck pipe is known to be influenced by drilling fluid properties and other parameters, such as the characteristics of rock formations. In this paper, we develop a support-vector-machine (SVM) based model to predict stuck pipe during drilling design and operations. To develop the model, we use a dataset, including stuck and nonstuck cases. In addition, we develop radial-base-function (RBF) neural network based model, using the same dataset, and compare its results with the SVM model. The results show that the performance of both models for prediction of stuck pipe does not differ significantly and both of them have highly accurate and can be used as the heart of an expert system to support drilling design and operations.

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Fig. 1

Our process to develop the classification model

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Fig. 2

Effect of stopping tolerance (epsilon) of SMO on number of support vectors

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Fig. 3

ROC curve of our model. We considered stuck cases as positive class and none stuck cases as negative class. The horizontal axis shows false positive values and vertical axis shows true positive values.

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Fig. 5

The structure of derived neural network. Hidden layer nodes squash function is sigmoid and the output node is linear. Also, nozzle size attribute is nominal and we set the correct value 1 and other related input nodes to zero.

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Fig. 4

ROC curve of the model on data set with stratiffed cross-validation. The curve shows average of the model on different 5 folds as unseen test data.



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