基于多层残差网络的地震提频处理在薄储集层识别中的应用

doi:10.7657/XJPG20240114

摘要/Abstract

摘要：

基于多层残差网络的地震提频处理方法，通过智能化网络将测井高频信息与地震数据相结合，能有效提升纵向分辨率，保持横向连续可追踪，利于薄储集层识别。针对AMH地区常规处理的地震数据仅能识别厚度大于30 m的碳酸盐岩层，无法有效识别厚度较小的薄储集层的问题，提出基于多层残差网络的地震提频处理方法，以井旁地震振幅作为训练数据，测井相对波阻抗作为训练标签，利用深度学习网络多层残差网络开展训练，获取相对波阻抗曲线的预测模型；通过将地震数据作为输入，利用深度网络训练模型计算得到相对波阻抗数据体，进而得到提频后的地震数据体相对应的反射系数体。通过对靶区地质情况的分析认识，对宽频子波进行标定后提取合适的宽频子波，与反射系数体进行褶积，得到提频后的地震数据体；利用提频后的地震数据体开展储集层反演，反演结果纵向具有较高分辨率，与主要目的层能够较好匹配，横向可以进行识别和追踪，利用高分辨地震数据反演结果实现AMH地区的薄储集层识别。结果表明，通过基于多层残差网络的地震提频处理及相应的高分辨模型反演，在AMH地区能够识别厚度大于10 m的薄储集层，较好地解决由于地震分辨率低无法识别薄储集层的问题，有效提高了薄储集层预测的精度，对同类型薄储集层识别具有借鉴意义。

关键词: 碳酸盐岩, 地震数据, 提频处理, 薄储集层, 多层残差网络, 相对波阻抗, 高分辨反演, 深度学习

Abstract:

The seismic frequency enhancement processing method based on multi-layer residual network combines high-frequency well logging information with seismic data through an intelligent network. This method effectively improves vertical resolution while maintaining lateral continuity，facilitating the identification of thin reservoir beds. In the AMH area，the seismic data processed by conventional techniques enable only the identification of carbonates thicker than 30 m，but not of thinner beds. The seismic frequency enhancement processing method based on multi-layer residual network was proposed for application in this area. First，a training was performed using the multi-layer residual network，a deep learning network，with the near-wellbore seismic amplitudes as training data and the relative wave impedance data from well logging as training labels. Thus，a predictive model for relative wave impedance curve was obtained. By using seismic data as input，the deep network training model was solved to obtain a relative wave impedance data cube，and then a data cube of reflection coefficient corresponding to the frequency-enhanced seismic data cube was obtained. After analyzing the geological conditions of the target area，appropriate wide-frequency wavelet was extracted after calibration，and then convolved with the reflection coefficient cube，so that a frequency-enhanced seismic data cube was obtained. Reservoir inversion was performed using the frequency-enhanced seismic data cube. The inversion results are of high resolution vertically，well matching the main target beds，and can be identifiable and traceable laterally. Ultimately，the identification of thin beds in the AMH area was realized through the application of high-resolution seismic inversion results. The seismic frequency enhancement processing based on multi-layer residual network together with the corresponding high-resolution model inversion can identify beds thicker than 10 m in the AMH area. This method effectively addresses the problem of infeasible thin bed identification using low-resolution seismic data，and improves the accuracy in predicting thin beds. It is referential for identifying similar thin beds.

Key words: carbonate, seismic data, frequency enhancement processing, thin bed, multi-layer residual network, relative wave impedance, high-resolution inversion, deep learning

中图分类号:

TE122

张文起, 李春雷. 基于多层残差网络的地震提频处理在薄储集层识别中的应用[J]. 新疆石油地质, 2024, 45(1): 102-108.

ZHANG Wenqi, LI Chunlei. Seismic Frequency Enhancement Processing Based on Multi-Layer Residual Network and Its Application to Identification of Thin Reservoirs[J]. Xinjiang Petroleum Geology, 2024, 45(1): 102-108.

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