Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/69149
Title: Application of fuzzy decision trees to reservoir recognition
Authors: Wang, XZ
Yeung, DS 
Tsang, ECC 
Lee, JWT 
Keywords: Weighted fuzzy rules
Learning
Decision trees
Approximate reasoning
Reservoir recognition
Issue Date: 2003
Publisher: Springer
Source: In X Yu & J Kacprzyk (Eds.), Applied decision support with soft computing, p. 364-390. Berlin, Heidelberg: Springer, 2003 How to cite?
Abstract: This chapter reports a real application of fuzzy decision tree to a reservoir recognition in the logging area for oilfield exploration. Reservoir fluid recognition is an important but difficult task in providing a comprehensive explanation for logging. A good recognition method can provide reliable evidence for building a standard of explanation in a region. Since there is much vagueness in the reservoir fluid recognition and there are considerable differences of geological structure in different regions, it is very difficult to establish a uniform mathematical model to recognize the reservoir. The commonly used methods for reservoir recognition include empirical formula, synthetic evaluation, fuzzy clustering, etc. Unfortunately, these methods fail to meet many applications’ requirements. For example, the empirical formula and synthetic evaluation methods could not handle fuzzy or vague data while the fuzzy clustering could not give a good recognition of oil-water layer. By applying the fuzzy decision tree induction method to the problem of reservoir recognition in an oilfield of northern China, we find the recognition results encouraging.
URI: http://hdl.handle.net/10397/69149
ISBN: 9783540370086 (ebook)
9783642535345 (print)
DOI: 10.1007/978-3-540-37008-6_16
Appears in Collections:Book Chapter

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