Saturday 23rd of September 2017
 

The Hybrid of Classification Tree and Extreme Learning Machine for Permeability Prediction in Oil Reservoir


Chandra Prasetyo Utomo

Permeability is an important parameter connected with oil reservoir. In the last two decades, artificial intelligence models have been used. The current best prediction model in permeability prediction is extreme learning machine (ELM). It produces fairly good results but a clear explanation of the model is hard to come by because it is so complex. The aim of this research is to propose a way out of this complexity through the design of a hybrid intelligent model. The model combines classification and regression. In order to handle the high range of the permeability value, a classification tree is utilized. ELM is used as a final predictor. Results demonstrate that this proposed model performs better when compared with support vector machines (SVM) and ELM in term of correlation coefficient. Moreover, the classification tree model potentially leads to better communication among petroleum engineers and has wider implications for oil reservoir management efficiency.

Keywords: Permeability Prediction, Extreme Learning Machine, Classification Tree, Hybrid Intelligent Systems, Oil Reservoir, Regression Problem

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ABOUT THE AUTHOR

Chandra Prasetyo Utomo
C. P. Utomo received B.Sc in Computer Science from University of Indonesia and M.S in Computer Science from King Abdullah University of Science and Technology (KAUST) in 2009 and 2011, respectively. Currently, he is researcher and lecturer at the Faculty of Information Technology Universitas YARSI, Jakarta. His research interests are artificial intelligence, machine learning, and data mining. Specifically, he is eager in applying computational intelligent methods such as neural networks, extreme learning machine, and support vector machines in real world problems in the specific domain such as classification, regression, association rules, and clustering.


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