Probabilistic Neural Network Inversion of Porosity Using Pre-Stack Multi-Attributes
详细信息
- 责任者：Wang Tongkui (The Exploration and Development Institute ; Jidong Oilfield ; Tangshan 063004 ; China) ; Zhao Baoyin
- 刊名：Computing Techniques for Geophysical and Geochemical Exploration
- 出版年：2013
- 卷期：35(2)
- 关键词：储层预测 ; reservoir prediction ; 地震属性 ; seismic attributes
- 页码：p.162-167
- 图表：illus., 1 table, 13 refs.
- 分类号：1600
- issnNo：ISSN1001-1749
- isbnNo：CN51-1242/P
摘要
Non-linear seismic inversion is always difficult problem.Some no-linear inversion method such as neural network,are being applied in the seismic interpreting.But they only base on post-stack seismic attributes.The paper developed a new method,which inverted porosity by method of PNN network basing on pre-stack inversion result and pre-stack seismic attribute.The process includes 3 steps.Firstly,some pre-stack attributes are extracted and the elastic parameters are inverted by pre-stack inversion.Secondly,analysis of the log data is carried out to acquire the relationship between porosity and pre-stack information.At last,with pre-stack inversion results and AVO attributes,PNN model is applied to get porosity volume.The technology eliminates the uncertainty of post-stack impendence inversion.The inverted porosity volume increases reservoir precision and is consistent with geologic result.