摘要
针对横波速度预测问题,在分析经验公式法和岩石物理建模法优缺点的基础上,结合横波速度预测原理,提出基于深度前馈神经网络方法(DFNN)进行横波速度的预测。研究从纵、横波速度关系入手,详细阐述了DFNN方法应用于横波速度预测的可行性,并介绍了该深度学习方法的基本原理;选择声波时差、密度、中子孔隙度、泥质含量、孔隙度5个储层参数与横波速度进行深度神经网络训练,建立可靠的横波速度预测模型。将该模型应用于不同研究区的横波速度预测,结果表明基于DFNN方法预测横波速度能够有效提高预测的精度和效率,适用范围广,可以为叠前AVO分析、叠前反演提供可靠的横波数据,具有较高的实际应用价值和推广意义。
Given the shear wave(S-wave)velocity prediction problem,the advantages and disadvantages of the empirical formula method and petrophysical modeling were analyzed,and the principle of s-wave velocity prediction was discussed.On this basis,this paper proposed a deep feedforward neural network(DFNN)for S-wave velocity prediction.Starting with the relationship between compressional wave(P-wave)and S-wave velocities,this study expounded the feasibility of applying the DFNN to S-wave velocity prediction and explained the principle of this deep learning method.Five reservoir parameters(acoustic time difference,density,neutron porosity,shale content,and porosity)were chosen for deep neural network training with S-wave velocity,and a reliable S-wave prediction model was thereby built.The model was applied to S-wave velocity prediction in different research areas,and the results show that DFNN-based S-wave velocity prediction achieves effectively improved prediction accuracy and efficiency and has a wide application range.It can provide reliable S-wave data for pre-stack amplitude-versus-offset(AVO)analysis and pre-stack inversion,so it is worth practical application and promotion.
作者
王树华
杨国杰
穆星
WANG Shuhua;YANG Guojie;MU Xing(Exploration and Development Research Institute,Shengli Oilfield Company,SINOPEC,Dongying City,Shandong Province,257015,China)
出处
《油气地质与采收率》
CAS
CSCD
北大核心
2022年第1期80-89,共10页
Petroleum Geology and Recovery Efficiency
基金
中国石化科技攻关项目“基于大数据技术的油藏精细表征方法研究”(P20071-1)。
