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Abstract
Journal of Petroleum Geology, vo1.
20(
3), July 1997, pp.
363-
372©Copyright 1997. Scientific Press, U.K. All rights reserved.
A
NEURAL-NETWORK APPROACH TO KNOWLEDGE-BASED WELL INTERPOLATION: A CASE STUDY OF A FLUVIAL
SANDSTONE RESERVOIR
P M. Wong*, D. Tamhane* and L. Wang*
This
paper presents a new approach to well interpolation using interpolation neural networks
(INETs). Traditional methods such as geostatistics have been applied to the spatial
mapping of reservoirs. However, these methods are not able to make use of qualitative
information, such as previously-constructed "expert knowledge" in the form of
iso-porosity-contours or structural maps of sand body geometry, in a simple manner.
This paper demonstrates the usefulness of INET
via a case study of a fluvial sandstone reservoir at an oilfield in the Asia-Pacific
region. The proposed method is applied to porosity interpolation based on data from
spatially-dispersed wells and regional geological knowledge. The results from this study
show that an INET is not only able to incorporate expert advice, but also that it is easy
to implement in a desktop computer or workstation. This allows an effective transfer of
geological knowledge to reservoir modelling.
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