Data mining by MOUCLAS algorithm for petroleum reservoir characterization from well logging data
Date
2004
Authors
Hao, Y.
Stumptner, M.
Quirchmayr, G.
He, Q.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
IFIP Advances in Information and Communication Technology, 2004, vol.154, pp.407-419
Statement of Responsibility
Conference Name
IFIP 18th World Computer Congress. TC12 First International Conference on Artificial Intelligence Applications and Innovations (AIAI-2004) (22 Aug 2004 : Toulouse, France)
Abstract
Petroleum reservoir characterization is one of the most difficult and challenging tasks of the exporation of petroleum industry and usually a long and costly procedure. This paper proposes a novel kind of patterns for the classification over quantitative well logging data, which is called MOUCLAS (MOUntain function based CLASsification) Patterns, based on the concept of the fuzzy set membership function which gives the new approach a solid mathematical foundation and compact mathematical description of classifiers. It integrates classification, clustering and association rules mining to identify interesting knowledge in the well logging database. The aim of the study is the use of MOUCLASS patterns to interpret the pay zones from well logging data for the purpose of reservoir characterization. This approach is better than conventional techniques for well logging interpretation that require a precise understanding of the relation between the well logging data and the underlying property of interest. © 2004 Springer Science + Business Media, Inc.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright status unknown