摘要
低阻油层作为一种非常规储层 ,其含油性受多个因素影响。常规测井解释方法评价低阻油层有很大的困难。人工神经网络具有自适应、自学习、抗干扰能力较强的特点。本文利用BP模型 ,结合低阻油层的电性特征 ,成功地对河南油区下二门油田的低阻油气层进行了识别。
As an unconventional one, low resistivity oil-bearing formation is affected by many factors and common logging interpretation method meets difficulty in evaluating the same formation. However, artificial nerve network features self-adapting, self-learning and strong disturbance resistant. Oil and gasformations are identified successfully with BP model combining the electrical properties of low resistivityoil formation.
出处
《特种油气藏》
CAS
CSCD
2001年第2期8-10,共3页
Special Oil & Gas Reservoirs
关键词
人工神经网络
BP模型
低阻油层
河南油区
下二门油田
油气勘探
artificial nerve network, BP model, low resistivity oil-bearing formation, identification,Henan oil province, Xia'ermen oilfield
