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

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.