摘要
准确的横波速度是叠前反演及叠前属性分析的必要信息,然而实际生产中横波速度资料往往匮乏,预测方法纷繁复杂且精度难以保证。通过选取自然伽马相对值、声波时差、密度和电阻率等参数,利用自适应BP神经网络方法,建立横波速度预测模型。经辽河地区实际资料证实,神经网络模型预测横波速度与实测横波速度吻合程度良好,能够满足生产需求。
Accurate shear wave velocity is the necessary information for prestack inversion and prestack attribute analysis, but it is always deficient in actual production. The prediction methods are numerous and complicated and the accuracy is difficult to ensure. By selecting the parameters such as relative natural gamma-ray value, acoustic slowness, density and resistivity, this paper used the method of self-adaptive BP neural network to establish the prediction model of shear wave velocity. The actual data in Liaohe Oilfield show the high precision of the prediction value, and the results can meet production requirements.
出处
《岩性油气藏》
CSCD
2013年第5期86-88,共3页
Lithologic Reservoirs
关键词
BP神经网络
横波速度
岩石物理
辽河油田
BP neural network
shear wave velocity
rock physics
Liaohe Oilfield
