地震属性优化与网络函数逼近储层砂体厚度预测方法及应用

The Method of Sand Body Thickness Prediction Based on Attribute Optimization and Network Function Approximation and Its Application

摘要: 砂体厚度(或含量)是油气勘探中的重要参数。以地震资料与测井解释成果为基础，研究储层砂体厚度预测方法，提出利用地震属性优化技术实现降维，建立敏感地震属性集；并将井旁地震道对应的敏感属性集与测井解释砂体厚度输入神经网络，通过网络训练使误差最小化；在该基础上，逐道输入敏感属性集，由网络输出对应的砂体厚度。在胜利油田H4区块应用上述方法预测砂岩厚度，相对误差基本小于20%，满足了勘探生产的需要。

Abstract: The sand body thickness (or content) was an important parameter in oil and gas exploration. Based on seismic data and well-logging interpretation, a method for predicting sand body thick-ness in reservoirs was proposed in this paper. A sensitive seismic attribute set was established by using attribute optimization and dimension reduction. A neural network was established with the input including sensitive attributes of borehole seismic trace and sand body thickness interpretation of well log data. The training of the network would minimize the error, and on this basis, the sensitive attribute was input for each trace and the corresponding sand body thickness was output through network. In Block H4 of Shengli Oilfield the above method is applied to predict sandstone thickness, and the relative error is less than 20%, and it basically meets the need of oil exploration and production.