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

• Main Author: Prasetyo Utomo, Chandra
• Other Authors: Moshkov, Mikhail
• Language: en
• Published: 2012
• Online Access: Prasetyo Utomo, C. (2011). The Hybrid of Classification Tree and Extreme Learning Machine for Permeability Prediction in Oil Reservoir. KAUST Research Repository. https://doi.org/10.25781/KAUST-Y0EY7; http://hdl.handle.net/10754/209392

Permeability is an important parameter connected with oil reservoir. Predicting the permeability could save millions of dollars. Unfortunately, petroleum engineers have faced numerous challenges arriving at cost-efficient predictions. Much work has been carried out to solve this problem. The main challenge is to handle the high range of permeability in each reservoir. For about a hundred year, mathematicians and engineers have tried to deliver best prediction models. However, none of them have produced satisfying results. 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. In this proposal, the system combines classification and regression models to predict the permeability value. These are based on the well logs data. In order to handle the high range of the permeability value, a classification tree is utilized. A benefit of this innovation is that the tree represents knowledge in a clear and succinct fashion and thereby avoids the complexity of all previous models. Finally, it is important to note that the ELM is used as a final predictor. Results demonstrate that this proposed hybrid 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 concerning this important process and has wider implications for oil reservoir management efficiency.