基于深度学习的古近系复杂沉积地区低频模型构建方法--以珠江口盆地LA油田为例

Development of low frequency model with multi-source information incomplex sedimentary strata based on deep learning method:Taking LA oilfield as an example

地震反演中低频模型的精度对提高反演结果的质量具有至关重要的作用,特别是对于断块构造复杂、沉积相带变化快的古近系陆相沉积地层,常规构建的低频模型会因井插值而产生“牛眼”现象,此外地震速度的不合理也会增加反演结果的不确定性。为了解决上述问题,以珠江口盆地LA油田为例,针对复杂构造和沉积背景下的地区,优化处理原始道集存在的剩余动校正、近道噪音等问题,开展基于常数模型的叠前同时反演,得到带限的纵波阻抗、纵横波速度比和密度等反演数据体;同时,针对地震速度与井速度低频趋势存在的差异,校正地震速度;利用深度学习的理论方法,将带限的反演数据体、校正后的地震速度体和测井曲线数据等信息融合,得到全频带的纵波阻抗、纵横波速度比和密度等反演数据体,作为后续反演低频模型的输入。研究结果表明,基于深度学习的低频模型可以很好地反映沉积相带变化特征,提高反演结果精度和可预测性,更好地表征储层的分布规律,明确储层的主体展布范围,为珠江口盆地LA油田后续开发项目的实施提供有力的技术支持和指导。

The accuracy of low-frequency model in seismic inversion plays an important role in improving the quality of inversion results,especially for lacustrine sedimentary strata during the Paleogene with complex tectonic faults or fast variation of sedimentary facies.The development of conventional low frequency model will produce“bull's eye”phenomenon due to well interpolation,or increase the uncertainty of inversion results due to unreasonable seismic velocity.In order to solve the above problems,taking LA oilfield with complex structural and sedimentary background as an example,band-limited P-wave impedance,P/S velocity ratio and density data volume can be obtained by optimizing the issues of residual moveout and near-offset multiples in the original gathers and carrying out pre-stack simultaneous inversion based on constant model;at the same time,due to the error between seismic velocity and the low frequency trend of the well velocity,seismic velocity must be corrected;and then,the band-limited inversion data volume,corrected seismic velocity volume and logging data are incorporated using the theoretical method of deep learning to obtain the full-band P-wave impedance,P/S velocity ratio and density inversion data volume,which are used as the low-frequency model for subsequent inversion.The study shows that the low-frequency model based on deep learning can well reflect the variation characteristics of sedimentary facies,and improve the accuracy and predictability of inversion results;it can also provide better characterization of hydrocarbon distribution in reservoirs and define the distribution of major payzone in the reservoir,providing strong technical support for the implementation of subsequent development projects in LA oilfield of the Pearl River Mouth Basin.