Cluster Analysis Of 3D Seismic Data For
Oil And Gas Exploration
Author(s)
D. R. S. Moraes, R. P. Espíndola, A. G. Evsukoff
& N. F. F. Ebecken
Abstract
Seismic attributes are information extracted from seismic data and constitute
important tools to estimate the geological structure of a place, helping the
understanding of the subsurface and reducing the uncertainties on interpretations.
This comprehension is crucial to tasks such as lithology prediction and reservoir
characterization. Seismic attributes are generated by transforming data from a
seismic line (two dimensional data) or a seismic volume (three dimensional
data). This work presents a study of clustering algorithms to these attributes and
the techniques employed follow two distinct approaches: a self organizing map
to perform crisp clustering and fuzzy c-means to perform partial clustering. The
evaluations of the partitions are performed with the PBM index which indicates
the best number of groups. Data from a Brazilian oil field is used to test the
algorithms.
Keywords: clustering, neural networks, fuzzy sets, cluster validity.
1 Introduction
In oil industry, the decision making of exploration and exploitation processes
involves dealing with geological, geophysical and geochemical data. The
evolution of computer hardware technology and the development of data base
management systems (DBMS) allow the data to processed and stored in very
huge databases. This fact represents a great opportunity for a company to record
all data from its operations. However, it makes the analysis and understanding of
such data difficult. Thus, Data Mining techniques may help the experts on their
task by automatically discovering useful information from these huge volumes of
data. Examples of activities that can be improved by the use of these techniques
Keywords
clustering, neural networks, fuzzy sets, cluster validity.
Related Book
Other papers in this volume
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