Seismic inversion is an important tool for reservoir characterization. The inversion results are significantly impacted by a reliable initial model. Conventional well interpolation methods are not able to meet the needs of seismic inversion for lateral heterogeneous reservoirs. Inspired by the sequence modelling network and seismic inversion in the Laplace–Fourier domain, we propose an initial model-building method using semi-supervised learning strategy. The proposed method considers spatial information to ensure the horizontal continuity of the initial model. Based on the fact that the low-frequency components of seismic signals in the Laplace–Fourier domain are easier to obtain, we use the forward model in the Laplace–Fourier domain to replace the time-domain forward model. The proposed workflow was validated using the Marmousi II model. Although the training was carried out on a small number of low-frequency impedance traces, the proposed workflow was able to build low-frequency model for the entire Marmousi II model with a correlation of 98%. Field data examples demonstrate the feasibility and effectiveness of the proposed method. For lateral heterogeneous reservoirs, the proposed method performs better than the well interpolation method. By utilizing the model obtained by the proposed method as the initial low-frequency model of the conventional inversion method, it is possible to estimate better inversion results. The results of different combinations of training sets demonstrate the stability of the proposed method. This method may still be a viable choice if there is lateral heterogeneity underground but not much well-logging label data.

中文翻译：

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基于半监督学习的地震反演初始模型构建

地震反演是储层表征的重要工具。可靠的初始模型对反演结果有显着影响。常规插井方法无法满足侧向非均质油藏地震反演的需要。受拉普拉斯-傅立叶域中的序列建模网络和地震反演的启发，我们提出了一种使用半监督学习策略的初始模型构建方法。该方法考虑空间信息以确保初始模型的水平连续性。基于拉普拉斯-傅里叶域地震信号的低频分量更容易获得的事实，我们使用拉普拉斯-傅里叶域正演模型代替时域正演模型。所提出的工作流程使用 Marmousi II 模型进行了验证。尽管训练是在少量低频阻抗迹线上进行的，但所提出的工作流程能够为整个 Marmousi II 模型构建低频模型，相关性达到 98%。现场数据实例证明了该方法的可行性和有效性。对于侧向非均质油藏，该方法的效果优于井插值方法。利用该方法得到的模型作为常规反演方法的初始低频模型，可以估计出更好的反演结果。训练集不同组合的结果证明了该方法的稳定性。如果地下存在横向非均质性但测井标签数据不多，则该方法可能仍然是一个可行的选择。