摘要：

断层作为油气运移、聚集的重要通道和场所，其识别任务是地震资料解释工作的重要环节。然而断层的类型多样、分布广泛、特征复杂，为断层识别任务带来了不小的困难，本文提出了使用3D TransUnet模型进行断层识别的方法，该模型基于3D CNN和Transformer模块构建而成，采用3D Unet模型端对端的结构设计，通过学习合成地震数据三维断层之间的空间关系，从而预测实际地震数据的断层信息，在荷兰北海F3区块和塔里木盆地哈拉哈塘地区的地震工区都成功应用，并取得良好的效果。研究结果表明, 3D TransUnet模型具有CNN局部精度高和Transformer全局注意力的特点，能够根据断层全局信息对复杂区域的断层进行推理预测。将实验结果与3D Unet模型和其他传统方法断层识别的结果进行对比，通过计算验证集断层识别的召回率(Recall)和精确率(Precision)，得到的3D TransUnet模型断层识别的召回率为0.87，精确率为0.83，远高于其他断层识别方法。在三维实际地震工区的应用中，3D TransUnet模型能够在不同实际地震工区都准确地识别出断层信息，对于特征较弱的断层，由于该模型加入了Transformer模块，具备全局注意力机制，因此可以通过整个工区断层的分布趋势来推断出该区域是否存在断层。通过将训练完成的断层识别模型同时运用到不同实际地震工区(F3区块和哈拉哈塘地区)，从而证明了该方法的通用性，即训练好的断层识别模型可以在不同地区的地震数据中使用。研究发现该方法能够有效地识别出地层中的微裂缝信息，在微裂缝作为储集层的油气田，由于微裂缝主要沿着大断层发育，井位都部署在大断层的附近，而在这类油气田的中后期采油阶段，井位的部署则主要根据微裂缝的发育程度决定，因此该断层识别方法对微裂缝作为储集层的油气田的井位部署具有指导意义。

关键词: 深度学习, 合成模型, 3D TransUnet, Transformer, 断层识别

Abstract:

Faults serve as crucial pathways and sites for hydrocarbon migration and accumulation, making their identification a key task in the interpretation of seismic data. However, the diversity of fault types, extensive distribution, and complex characteristics pose significant challenges to fault identification. To address this issue, this paper proposes a fault identification method using a 3D TransUnet model. Constructed based on 3D CNN and transformer modules, this model adopts an end-to-end structural design of the 3D Unet architecture. By learning the spatial relationships of three-dimensional faults in synthetic seismic data, it directly predicts fault information in actual seismic data. The method has been successfully applied to seismic work areas in the F3 block of the Dutch North Sea and the Halahatang area of the Tarim Basin, achieving excellent results. The research findings demonstrate that the 3D TransUnet model combines the high local accuracy of CNN and the global attention mechanism of Transformer, enabling inference and prediction of faults in complex regions based on global fault information. Compared with the 3D Unet model and other traditional fault identification methods, the 3D TransUnet model achieved a recall rate of 0.87 and a precision rate of 0.83 on the validation set, significantly outperforming other approaches. In practical applications within three-dimensional seismic work areas, the 3D TransUnet model accurately identifies fault information across different regions. For faults with subtle features, the incorporation of the Transformer module equips the model with a global attention mechanism, allowing it to infer the presence of faults by analyzing distribution trends across the entire work area. By applying the trained fault identification model to different practical seismic work areas (the F3 block and the Halahatang area in this study), the universality of the method is demonstrated, indicating that the trained fault identification model can be effectively utilized across seismic data from various regions. This study finds that the method can effectively identify microfracture information within formations. In oil and gas fields where microfractures serve as reservoirs, since microfractures primarily develop along major faults, well locations are typically deployed near these large faults. However, during the middle and late stages of oil production in such fields, well placement decisions rely more heavily on the development degree of microfractures. Therefore, this fault identification method provides valuable guidance for well placement in oil and gas fields where microfractures act as reservoirs.

Key words: deep learning, synthetic model, 3D TransUnet, transformer, fault identification

中图分类号: 

• P618.13

• P315.3