摘要
杨楼油田杨浅19断块常规测井曲线在对岩性进行识别时,各类测井曲线对岩性识别都有一定影响,采用简单的反映线性关系的交会图和直方图难以达到岩性定量精细识别的目的。通过对杨浅19断块岩性识别模型的研究,确立了人工神经网络的岩性识别模型,提高了该区的岩性辨识能力,使识别结果更加准确、可信。
The conventional logging curves of Yangliao 19 fault block in Yanglou Oilfield have various influences on lithology identification when identifying lithology.It is difficult to achieve the lithologic identification by simple logarithmic and histogram reflecting the linear relationship The purpose of quantitative identification of lithology.In this paper, the lithology identification model of Yang-19 fault block is studied, and the lithology identification model of artificial neural network is established to improve the lithology identification ability of the area, making the recognition result more accurate and credible.
出处
《化工设计通讯》
CAS
2018年第1期222-222,共1页
Chemical Engineering Design Communications
关键词
岩性
测井曲线
岩性识别
模型
lithology
logging curve
lithology identification
model
