口译(哲学)
储层建模
地质学
表征(材料科学)
人工神经网络
地震学
计算机科学
人工智能
石油工程
材料科学
纳米技术
程序设计语言
作者
V. Singh,Avanish Kumar Srivastava,Dhanesh Tiwary,P. K. Painuly,Mahesh Chandra
出处
期刊:The leading edge
[Society of Exploration Geophysicists]
日期:2007-10-01
卷期号:26 (10): 1244-1260
被引量:22
摘要
Modern 3D seismic data and the associated extracted attributes have allowed better description of reservoir heterogeneities and more realistic assessment of hydrocarbons in place. However, the establishment of a complicated nonlinear relationship between seismic attributes and reservoir properties has been a major challenge for working geoscientists. Recently, supervised neural networks have been used for predicting reservoir properties away from the boreholes in interwell regions after establishing the relationship between seismic attributes and well-log data. The effectiveness of these neural network techniques in 3D seismic interpretation is demonstrated in this paper through a real data example from India's Cambay Basin.
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