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A NEURAL‐NETWORK APPROACH TO KNOWLEDGE‐BASED WELL INTERPOLATION: A CASE STUDY OF A FLUVIAL SANDSTONE RESERVOIR
Author(s) -
Wong P. M.,
Tamhane D.,
Wang L.
Publication year - 1997
Publication title -
journal of petroleum geology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.725
H-Index - 42
eISSN - 1747-5457
pISSN - 0141-6421
DOI - 10.1111/j.1747-5457.1997.tb00641.x
Subject(s) - interpolation (computer graphics) , geostatistics , fluvial , geology , artificial neural network , workstation , geomorphology , computer science , artificial intelligence , image (mathematics) , spatial variability , statistics , mathematics , structural basin , operating system
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.