论文详情
走滑断层精细识别是断控缝洞型碳酸盐岩油气藏勘探开发的关键环节之一, 但走滑断层的水平位移在垂直断层走向的地震剖面上不易识别, 为此, 提出了一种基于深度残差网络的走滑断层智能识别方法。该方法的网络模型由特征提取子网络、结构提取子网络和去噪卷积子网络3个子网络构成。特征提取子网络提取地震与断层预测映射的残差特征, 结构提取子网络提取边界结构的残差映射实现断层解释的目标, 去噪卷积子网络去除网络累计生成的噪声。网络在预测时采用了多层输出融合技术和迁移学习的方式, 能有效避免高频特征信息的丢失, 增强对不同规模断层分类解释的鲁棒性和泛化力。通过对合成记录验证分析可知, 该方法对低信噪比地震资料情况下的小断距、弱走滑断层的识别精度高, 预测的损失率低, 预测断层连续性好, 断层边界清晰, 且抗噪性较好。塔里木盆地富满地区果勒西区块实际地震资料的应用结果表明, 该方法对线性走滑断层、压扭辫状走滑断层和拉张辫状走滑断层等不同性质的走滑断层均有较好的识别效果。
Strike-slip fault identification is important to the exploration and development of fault-controlled fractured-vuggy carbonate reservoirs, but the horizontal displacement of a strike-slip fault is ambiguous on seismic sections perpendicular to fault strike. Manual fault interpretation with heavy workload is greatly dependent on the experience of the interpreter. We propose an intelligent method for strike-slip fault identification based on the deep residual network. The residual network is composed of three sub-networks for feature extraction, structure extraction, and denoising convolution, respectively. The sub-network of feature extraction is used to extract residual mapping features of seismic and fault prediction. The sub-network of denoising convolution is used to remove accumulated noises generated by the network. The sub-network of structure extraction is used to extract the residual mapping of boundary structure for fault interpretation. Multi-layer output fusion and transfer learning are adopted to avoid high-frequency loss in network-based prediction and enhance the robustness and generalization of fault classification and interpretation of different scales. Model tests using synthetic records show high accuracy, small missing rate, good continuity, distinct boundaries, and good anti-noise performance of identifying the faults with small displacement and strike slip on seismic sections with low signal-to-noise ratio. The field data application to fault-controlled fractured-vuggy carbonate reservoirs in Fuman Oilfield, the Tarim Basin shows good results of identifying linear strike-slip faults, compressional torsional braided strike-slip faults, and extensional braided strike-slip faults.