NDLI: An Application of the BP Neural Network to Carbonate Karst Reservoirs Prediction

Content Provider	IEEE Xplore Digital Library
Author	Yixin Yu Jinchuan Zhang Zhijun Jin
Copyright Year	2011
Abstract	Effective porosity is one of the most important parameters in reservoir predication, especially in the carbonate karst reservoirs. In contrast to the calculated results by conventional statistical models, the BP neural network model can predict the porosity of reservoir more accurately because of its high nonlinear mapping ability and very strong abilities of self-adaptation and self-study. In this article, the author unified the different sampling interval of seismic and well logging responses by the mathematical method. Then discussed the correlation of them by the multiple linear regression. On that basis, the authors established the BP neural network model to predict the effective porosity of the reservoirs. The results shows that the porosity and the developed zone of fracture can be predicted in combination of three attributes of seismic and well logging data, moreover, the result is comparatively consistent well with the actually measured porosity and the well performance in study area.
Starting Page	133
Ending Page	136
File Size	520054
Page Count	4
File Format	PDF
ISBN	9781457710858
DOI	10.1109/ISCID.2011.135
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Geology Predictive models carbonate karst reservoirs Training seismic responses BP neural network correlation Coherence Reservoirs Attenuation well logging responses multiple linear regression
Content Type	Text
Resource Type	Article

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