摘要
苏14井区属苏里格气田,地质情况十分复杂,主要表现为砂岩发育,但有效砂岩非均质性强,横向变化大,厚度较薄,在垂向上分布比较分散。属性分析已是目前寻找非构造油气藏的有效手段之一。本文利用叠前地震资料、叠前纵横波联合反演结果和叠后属性进行属性融合,不但保留了叠前所含的流体信息,而且利用了叠后数据的高信噪比特点。采用单步递归方法从多个属性中寻找最优的属性组合,利用概率神经网络方法训练测井曲线与地震属性之间的非线性关系,并用训练样本预测苏14井区的自然咖玛,提高地震资料的分辨率,较好地预测苏14井区的有效储层分布范围,为储层精细描述提供依据。
In this study area the geological conditions are complicated and the effective sandstone is very heterogeneous.The sandstones are thin and lateral and vertical variations are large.We introduce multi-attribute fusion technology based on pre-stack seismic data, pre-stack P-and S-wave inversion results,and post-stack attributes.This method not only can keep the fluid information contained in pre-stack seismic data but also make use of the high SNR characteristics of post-stack data.First,we use a one-step recursive method to get the optimal attribute combination from a number of attributes.Second,we use a probabilistic neural network method to train the nonlinear relationship between log curves and seismic attributes and then use the trained samples to find the natural gamma ray distribution in the Su-14 well block and improve the resolution of seismic data.Finally,we predict the effective reservoir distribution in the Su-14 well block.
基金
The National Natural Science Foundation of China and China Petroleum&Chemical Corporation Co-funded Project(Grant Nos 40839905 and 40739907)
关键词
多属性融合
神经网络
交互验证
苏14井区
multiple attributes fusion
neural network
interactive validation
Su-14 well block
