基于深度卷积神经网络的地震数据溶洞识别

溶洞识别对于缝洞型油气藏的勘探与开发具有重要意义。传统溶洞识别方法多解性强且效率低,因此将具有强特征学习能力、高泛化性的深度学习方法引入溶洞识别中,但溶洞的地震波场响应特征复杂、异常体尺寸较小、训练样本难以获取等导致深度学习在识别溶洞时仍具挑战性。为此,提出一套识别地震数据溶洞的"两步法"深度学习方法:首先通过U-Net模型识别地震剖面上的"串珠状"异常反射;再根据"串珠状"异常识别结果对地震数据进行小范围截取,输入深度残差网络中,实现对实际溶洞轮廓的预测。对于实际溶洞预测训练数据难以获取这一问题,采用波动方程正演模拟的方法制作具有准确标签的溶洞地震数据。实际地震数据的应用表明,该方法对于溶洞识别准确性高,抗噪能力强,可以极大地节约人工解释成本。

Identification of Karst caves in seismic data based on deep convolutional neural network

Karst cave identification is significant for the exploration and development of fracture-cavity oil and gas reservoirs.Conventional identification methods are multi-solution and inefficient.Therefore,a deep learning method with strong feature learning and generalization capabilities is introduced into Karst cave identification.However,it is still a challenging task to identify Karst caves by deep learning due to the complex response characteristics of Karst caves to the seismic wavefield,the small sizes of anomalies,and the difficulties in obtaining training samples.Faced with this problem,we propose a“two-step”deep learning method for identifying Karst caves in seismic data.Specifically,the U-Net model is used to identify the“bead-shaped”anomalous reflection on the seismic section.Then,according to the identification results of the“bead-shaped”anomalies,seismic data are cropped into small patches and input into the deep residual network to implement the prediction of the actual Karst cave profile.Considering the difficulties in obtaining training data for actual Karst cave prediction,we propose implementing wave equation forward modeling to generate seismic Karst cave data with accurate labels.The application of field seismic data shows that the method is accurate in Karst cave identification,has strong noise resistance,and can greatly save the cost of manual interpretation.