Отправлено: 28.07.13 05:24. Заголовок: The Hybrid of Classification Tree and Extreme Learning Machine for Permeability Prediction in Oil R

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


Abstract
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


IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 1, No 1, January 2013
ISSN (Print): 1694-0784 | ISSN (Online): 1694-0814