Fuzzy classifier fusion: an application to reservoir facies identification

• Published in: Neural computing & applications 2018-08, Vol.30 (3), p.825-834
• Main Authors: Mollajan, Amir, Memarian, Hossein, Nabi-Bidhendi, Majid
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Bayesian analysis; Classifiers; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Original Article; Performance enhancement; Probability and Statistics in Computer Science; Support vector machines
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-016-2690-0

An application of classifier fusion technique is presented to improve the performance of automated reservoir facies identification system. The algorithm presented in this study uses three well-known classifiers, namely Bayesian, k -nearest neighbor (kNN), and support vector machine (SVM) to automatically identify four defined facies of Asmari Formation from log-derived amplitude versus offset (AVO) attributes. Fuzzy Sugeno integral (FSI) method is then employed to combine the outputs of three investigated classifiers and increase the consistency of reservoir facies identification process. The experimental results obtained from applying the presented algorithm on data related to three wells drilled in Asmari Formation provide evidence of the effectiveness of the proposed algorithm regarding true positive (TP), false positive (FP), and classification accuracy criteria.