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Soft computing for qualitative and quantitative seismic object and reservoir property prediction. Part 2: Fuzzy logic applicationsNormal access

Authors: F. Aminzadeh and D. Wilkinson
Journal name: First Break
Issue: Vol 22, No 4, April 2004
Language: English
Info: Article, PDF ( 625.24Kb )
Price: € 30

Summary:
This is the second instalment of the series of review papers on soft computing applications in the petroleum industry. In this paper Fred Aminzadeh and David Wilkinson focus on fuzzy logic applications, including a brief overview of fuzzy logic technology, recent applications of fuzzy logic in various exploration and development scenarios, and a proposed framework to use fuzzy logic for seismic stratigraphy analysis and to explore applications of fuzzy differential equations. In the companion paper to this series (Aminzadeh and de Groot, 2004), the main advantages of soft computing were highlighted. Among them were integrating information from various sources with varying degrees of uncertainty. Geosciences data used in exploration are inherently imprecise, uncertain and fuzzy. This, combined with many linguistic rules and subjective treatment of the data, make it a good candidate for the use of fuzzy set theory for the processing, analysis and interpretation of E&P data. Figure 1, from Wilkinson et al (2003), illustrates the difficult task of modelling and analyzing the mother earth (geologic outcrops) with numerical (in this case seismic) measurements. The main advantage of fuzzy logic is its versatility in combining the quantitative data and qualitative information and subjective observation and rules. Given the nature of the information available for interpretation (such as seismic data, well logs, geological and other geosciences data) fuzzy sets theory can help in developing an appropriate framework to carry out quantitative analysis of the information and data which are the aggregate of both qualitative and quantitative types. After a brief overview of prior work, we give several examples of applications of fuzzy logic in exploration.


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