摘要
Abstract
Hydrocarbon reservoirs have been discovered in strike-slip fault zones across multiple domestic basins,verifying the enormous exploration potential of such tectonic settings.The Lishu Fault Depression in the Songliao Basin hosts a complex assemblage of strike-slip faults;to assess the hydrocarbon accumulation potential of this region,accurate identification and classification of fracture volumes represent a core prerequisite.Genetically,these fracture volumes can be divided into three categories,namely fault-fracture bodies,fault-fold bodies,and fold-fracture bodies.The Gradient Structure Tensor(GST)attribute is well suited to capturing discontinuous seismic responses,and thus yields superior performance in fault and fracture identification.Against this backdrop,this paper proposes a GST-based seismic structural attribute method for fracture volume prediction.First,structure-oriented filtering was applied to the raw seismic data to enhance signal-to-noise ratio,which effectively suppressed noise interference in the vicinity of fault zones.Second,GST-derived coherence and curvature attributes were computed,which demonstrated higher characterization precision than their conventional counterparts.Finally,to mitigate the ambiguity inherent in single-attribute interpretation,a multi-attribute fusion strategy integrating GST-based coherence,curvature,and chaotic attributes was implemented.This integrated approach enabled the precise delineation of the effective distribution ranges of the three fracture volume types,thereby achieving multi-scale characterization of fracture volume development in the Lishu Fault Depression.关键词
梨树断陷/储层预测/裂缝体/梯度结构张量/地震属性Key words
Lishu Fault Depression/reservoir prediction/fracture volume/gradient structure tensor(GST)/seismic attribute分类
能源科技
