澳门银河赌场注册送38-澳门银河赌场招人好招吗_百家乐德州_全讯网下载 (中国)·官方网站

Abstract: Accurate characterization of the fault system is crucial for the exploration and development of fractured reservoirs. The fault characterization technique based on multi-azimuth and multi-attribute fusion is a hotspot. In this way, the fault structures of different scales can be identified and the characterization details of complex fault systems can be enriched by analyzing and fusing the fault-induced responses in multi-azimuth and multi-type seismic attributes. However, the current fusion methods are still in the stage of violent information stacking in utilizing fault information of multi-azimuth and multi-type seismic attributes, and the fault or fracture semantics in multi-type attributes are not fully considered and utilized. In this work, we propose a physic-guided multi-azimuth multi-type seismic attributes intelligent fusion method, which can mine fracture semantics from multi-azimuth seismic data and realize the effective fusion of fault-induced abnormal responses in multi-azimuth seismic coherence and curvature with the cooperation of the deep learning model and physical knowledge. The fused result can be used for multi-azimuth comprehensive characterization for multi-scale faults. The proposed method is successfully applied to an ultra-deep carbonate field survey. The results indicate the proposed method is superior to self-supervised-based, principal-component-analysis-based, and weighted-average-based fusion methods in fault characterization accuracy, and some medium-scale and microscale fault illusions in multi-azimuth seismic coherence and curvature can be removed in the fused result.