Extreme Learning Machine for Reservoir Parameter Estimation in Heterogeneous Sandstone Reservoir. (26th May 2015)

• Record Type: Journal Article
• Authors: Cao, Jianhua; Yang, Jucheng; Wang, Yan; Wang, Dan; Shi, Yancui
• Other Names: Sun Zhan-li Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2015(2015)
• Publication Date: 2015-05-26
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2015/287816
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.