地震储集层预测的基本内容可以归结为岩相预测、岩性预测、物性预测及含油气性预测。
The basic content of seismic reservoir prediction covers a large prediction range from petrofacies, lithology, petrophysics, to oil and gas distribution.
通过实例介绍了利用一种概率神经网络技术预测储层物性参数的方法。
Using an example, a method based on probabilistic neural network technique is introduced, which aims at prediction of petrophysical parameters for reservoir.
声波阻抗参数直接反映油气储层物性，能用于预测储层空间和储集条件分布。
As a parameter directly reflecting the petrophysical property of reservoir, the acoustic impedance can be used for predicting reservoir space distribution and storage conditions.
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