Abstract
Strike-slip faults can play an important role in hydrocarbon accumulation.Therefore advanced studies on the interpretation of intra-basinal strike-slip faults are crucial.Hybrid attributes computed from conditioned 3D seismic data via artificial neural networks may enhance subsurface fault images.Jurassic formations within the C36 3D Prospect located in the Central Depression of the Junggar Basin are important to hydrocarbon accumulation.However,the geometry of strike-slip faults and their relationship with hydrocarbon accumulation are inadequately understood.The present study highlights these strike-slip faults using 3D seismic data from the C36 3D Prospect.Several conditioning approaches have enabled enhanced imaging of the strike-slip faults using computed hybrid attributes.They are based on implementing a dip-steering cube extracted from the original seismic data,along with analyses of multiple seismic attributes and a supervised neural network.Analysis of the hybrid attributes reveals the development of isolated,soft-linked,hard-linked,and coalesced faults along an approximately NE-SW trend.Various types of interactions were observed between the upper and lower fault systems.The different types of vertical linkages between the strike-slip faults provide hydrocarbon migration paths and thus,contribute differently to hydrocarbon migration.
NSTL