石油物探2024，Vol.63Issue(1)：129-137,9.DOI:10.12431/issn.1000-1441.2024.63.01.011
基于深度残差网络的走滑断层智能识别方法
Intelligent identification of strike-slip faults based on deep residual network:A case study in Fuman Oilfield,Tarim Basin
摘要
Abstract
Strike-slip fault identification is important to the exploration and development of fault-controlled fractured-vuggy car-bonate 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 in-telligent 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 ex-traction 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 residu-al 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 sig-nal-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.关键词
断控缝洞体/碳酸盐岩油气藏/残差网络/深度学习/走滑断层/智能识别Key words
fault-controlled fractured-vuggy reservoir/carbonate reservoir/residual network/deep learning/strike-slip fault/intelli-gent identification分类
天文与地球科学引用本文复制引用
孙冲,毕建军,雷刚林,张银涛,康鹏飞,谢舟,郑明君,曹佳佳,赵海山,陈彦虎..基于深度残差网络的走滑断层智能识别方法[J].石油物探,2024,63(1):129-137,9.基金项目
中国石油天然气股份有限公司科学研究与技术开发项目(2021DJ1501)资助.This research is financially supported by the Scientific Research and Technology Development Project of CNPC(Grant No.2021DJ1501). （2021DJ1501）
