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Mathematical Problems in Engineering
Volume 2015, Article ID 287816, 10 pages
Research Article

Extreme Learning Machine for Reservoir Parameter Estimation in Heterogeneous Sandstone Reservoir

College of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, China

Received 21 August 2014; Revised 8 November 2014; Accepted 10 November 2014

Academic Editor: Zhan-li Sun

Copyright © 2015 Jianhua Cao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study focuses on reservoir parameter estimation using extreme learning machine in heterogeneous sandstone reservoir. The specific aim of work is to obtain accurate porosity and permeability which has proven to be difficult by conventional petrophysical methods in wells without core data. 4950 samples from 8 wells with core data have been used to train and validate the neural network, and robust ELM algorithm provides fast and accurate prediction results, which is also testified by comparison with BP (back propagation) network and SVM (support vector machine) approaches. The network model is then applied to estimate porosity and permeability for the remaining wells. The predicted attributes match well with the oil test conclusions. Based on the estimations, reservoir porosity and permeability have been mapped and analyzed. Two favorable zones have been suggested for further research in the survey.