Journal
PETROLEUM SCIENCE AND TECHNOLOGY
Volume 30, Issue 19, Pages 1998-2007Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/10916466.2010.518187
Keywords
artificial neural networks; carbonate reservoirs; depth matching; permeability; reservoir zoning
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The distinct characteristics of Iranian oil reservoirs, such as high pressure, heterogeneity and anisotropy, high thickness, carbonation, huge size, and presence of cracks and various rock types, lead to deficiency of present applications of neural network methods. The authors attempt to improve the proficiency of present methods in one of Iranian heterogeneous oil reservoirs for permeability prediction using the well logging data, by zoning the reservoir on the basis of geology characteristics and sorting the data in correspondence. The obtained results from the well logging data using artificial neural networks are compared with the measured permeability in core analysis experiments. The appropriate compatibility of the results confirms the proposed method.
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