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Integration of seismic and well-log data using statistical and neural network methods

Y. Zee Ma, Ernest Gomez and Barbara Luneau
Integration of seismic and well-log data using statistical and neural network methods
Leading Edge (Tulsa, OK) (April 2017) 36 (4): 324-329

Abstract

In the last two to three decades, the use of seismic attributes for reservoir characterization and modeling has grown exponentially. Now, a dozen or more attributes are often extracted from seismic data to predict reservoir properties. Meanwhile, an increasing trend of acquiring more wireline logs provides more and more data to describe reservoir properties. Both statistical methods and artificial neural networks (ANNs) are often used to extract information and make predictions. Although statistical methods and ANNs provide powerful tools for geoscience data integration, they also have pitfalls. We present principal component analysis (PCA) and ANNs for facies classifications and porosity prediction. We also show the use and limitations of these methods and the importance of integrating the geologic and petrophysical knowledge.


ISSN: 1070-485X
EISSN: 1938-3789
Serial Title: Leading Edge (Tulsa, OK)
Serial Volume: 36
Serial Issue: 4
Title: Integration of seismic and well-log data using statistical and neural network methods
Affiliation: Schlumberger, International
Pages: 324-329
Published: 201704
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 12
Accession Number: 2017-034999
Categories: Economic geology, geology of energy sourcesApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus.
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2017, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 201720
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