测井技术2012，Vol.36Issue(4)：421-425,430,6.
基于BP神经网络技术的储层流动单元研究
Research on Reservoir Flow Unit Based on BP Neural Network Technology
司马立强 1肖华 1袁龙 1陆凤才2
作者信息
- 1. 西南石油大学资源与环境学院,四川成都610500
- 2. 江苏油田勘探局地质测井处,江苏扬州225002
- 折叠
摘要
Abstract
Fang 4 block reservoir with low porosity and low permeability is complex in Huangjue oilfield, so, reservoir parameter calculated has bigger error. Combining with coring formation property material and log data, the reservoir is divided into three types of flow units by flow zone index (IFZ). Established are the recognition and division standards of the flow units. Based on this, BP neural network technology is used to learn and train the reservoir flow units of coring wells. With such a technology, directly built is the mapping relation between log responses and flow unit types so as to learn and predict the flow units in the coring wells or non-coring wells. Log interpretation accuracy is obviously improved, which provides an effective way for fine reservoir interpretation.关键词
测井解释/流动单元/低孔隙度/低渗透率/流动带指数/BP神经网络/黄珏油田Key words
log interpretation/ flow unit/ low porosity/ low permeability/ flow zone index/ BP neural network/ Huangjue oilfield分类
天文与地球科学引用本文复制引用
司马立强,肖华,袁龙,陆凤才..基于BP神经网络技术的储层流动单元研究[J].测井技术,2012,36(4):421-425,430,6.
