Neural network seismic prediction of sand and mudstone lithology of Pinghu Formation in Xihu Sag
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摘要: 传统的地震波阻抗反演方法存在岩性分辨能力不高和多解性问题,反演结果难以满足精细刻画岩性分布规律的要求。本文通过构建包含岩性和波阻抗信息的归一化后的拟伽马曲线作为岩性指示曲线,利用神经网络方法,将地震数据转化为与岩性关系更密切的伽马数据体。通过神经网络地震反演,得到砂泥岩岩性反演数据体。将该方法用于西湖凹陷平湖组砂泥岩岩性反演,与传统方法相比,泥岩厚度预测精度达93%,较为准确地刻画了地下砂泥岩分布情况,为后期的油气勘探提供依据。Abstract: The traditional seismic P-wave impedance inversion method has the problems of low lithologic resolution and multi-solution,and it is hence difficult for the inversion results to meet the requirements of finely characterizing the lithologic distribution.In this paper,by constructing a normalized pseudo-gamma curve containing lithology and P-wave impedance information as a lithology index indicator curve,the neural network method is used to convert seismic data into a gamma data volume which is more closely related to lithology.Through the neural network seismic inversion,the sand and mudstone lithologic inversion data volume is obtained.This method was used to invert the sand and mudstone lithology of the Pinghu Formation in the Xihu Sag.Compared with traditional methods,the prediction accuracy of the mudstone thickness is up to 93%,which more accurately characterizes the distribution of underground sand and mudstone,and provides a basis for later oil and gas exploration.
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