摘要
建立在经验公式和线性假设基础上的常规测井解释方法其精度和成功率都较低。人工神经网络技术具有自适应、自学习的功能，其在测井解释研究中具有良好的应用前景。本文以实例说明了神经网络技术在测井孔隙度解释中所取得的成果。
The normal logging explaination methods based on experience formulas and the linear hypothesis show lower accuracy and successful rate. The artificial neural networks technique can adapt itself, learn itself. It has well prospect in logging explaination. This paper provides a case to illustrate the advantage of the artificial neural network technique in logging porosity explaination.
出处
《石油实验地质》
CAS
CSCD
北大核心
1999年第2期166-169,共4页
Petroleum Geology & Experiment
