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The AAPG/Datapages Combined Publications Database
AAPG Bulletin
Abstract
AAPG Bulletin, V.
Estimation of lithologies and depositional facies from wire-line logs
M. M. Saggaf,1 Ed L. Nebrija2
1Massachusetts Institute of Technology, MIT E34-462, 42
Carleton Street, Cambridge, Massachusetts 02142; email: [email protected]
2Saudi Arabian Oil Company, P.O. Box 12323, Dhahran 31311, Saudi Arabia; ;
email: [email protected]
AUTHORS
M. M. Saggaf has a B.Sc. in mathematics (1989) from King
Fahd University of Petroleum and Minerals and an M.Sc. (1996) and Ph.D. (2000) in
geophysics from the Massachusetts Institute of Technology (MIT). He has been working for
Saudi Aramco in the Reservoir Characterization and RD departments since 1989. His areas of
interest include signal analysis
, wave propagation, inversion, reservoir characterization,
neural networks, and fuzzy logic. He is a member of AAPG and the Society of Exploration
Geophysicists.
Ed L. Nebrija has a B.Sc. in physics from University of the Philippines and a Ph.D. in geophysics form the University of WisconsinMadison. From 1979 to 1992, he worked for Shell Offshore, Inc. in New Orleans, Louisiana, in various capacities as marine seismic party chief, exploration geophysicist, and exploitation geophysicist. Since 1992, he has been a staff member of the Reservoir Characterization Department of Saudi Aramco. He is a member of AAPG, the Society of Exploration Geophysicists, and the European Association of Geoscientists and Engineers.
ACKNOWLEDGMENTS
We would like to thank Saudi Arabian Oil Company (Saudi Aramco) for supporting this research and for granting us permission for its publication.
ABSTRACT
We approach the problem of identifying facies from well
logs
through the use of neural networks that perform vector quantization of input data by
competitive learning. The method can be used in either an unsupervised or supervised
manner. Unsupervised
analysis
is used to segregate a
well
into distinct facies classes
based on the
log
behavior. Supervised
analysis
is used to identify the facies types
present in a certain
well
by making use of the facies identified from cores in a nearby
well
. The method is suitable for analyzing lithologies and depositional facies of
horizontal wells, which are almost never cored, especially if core data is available for
nearby vertical wells. Both modes are implemented and used for the automatic facies
analysis
of horizontal wells in Saudi Arabia. In addition to the identification of facies,
the method is also able to calculate, for each
analysis
, confidence measures that are
indicative of how
well
the
analysis
procedure can identify those facies given
uncertainties in the data. Moreover, we can apply constraints derived from human
experience and geologic principles to guide the inference process.
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