A self-generating fuzzy rules inference system for petrophysical properties prediction

Author

Fung, C.C. ; Wong, K.W. ; Wong, P.M.

Author_Institution

Sch. of Electr. & Comput. Eng., Curtin Univ. of Technol., Bentley, WA, Australia

Volume

1

fYear

1997

fDate

28-31 Oct 1997

Firstpage

205

Abstract

This paper discusses the application of a self-generating fuzzy rule extraction and inference system for the prediction of petrophysical properties from well log data. A set of core data with known characteristics is first selected as the training samples. Fuzzy rules are then extracted and undergo a process of rule elimination. The reduced rule set forms the rule-base of the fuzzy prediction model. This will be used to predict properties of other depths within or around the well. Results based on a test case for the prediction of porosity is reported and the performance of the system is discussed

Keywords

fuzzy logic; geology; geophysics computing; inference mechanisms; knowledge based systems; learning (artificial intelligence); petroleum industry; uncertainty handling; fuzzy prediction model; fuzzy rule extraction; performance; petroleum wells; petrophysical property prediction; porosity; rule base; rule elimination; self-generating fuzzy rule inference system; training samples; well log data; Artificial neural networks; Australia; Data mining; Fuzzy systems; Instruments; Laboratories; Neural networks; Petroleum; Predictive models; Well logging;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-4253-4

Type

conf

DOI

10.1109/ICIPS.1997.672766

Filename

672766