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.
Intelligent Classifier Approach for Prediction and Sensitivity Analysis of Differential Pipe Sticking: A Comparative Study
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.
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Jahanbakhshi, R., and Keshavarzi, R. (March 10, 2016). "Intelligent Classifier Approach for Prediction and Sensitivity Analysis of Differential Pipe Sticking: A Comparative Study." ASME. J. Energy Resour. Technol. September 2016; 138(5): 052904. https://doi.org/10.1115/1.4032831
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