Fracture Identification in a Tight Sandstone Reservoir: A Seismic Anisotropy and Automatic Multisensitive Attribute Fusion Framework

Abstract

Fracture monitoring is crucial for many geo-industrial applications, such as carbon dioxide storage and hydrocarbon exploration in tight reservoirs, because fractures can form storage space or leaking paths for geological sealing. We propose a fracture identification framework for geo-industrial applications by exploiting seismic reflection anisotropy and automatic multisensitive attribute fusion. Anisotropy maps extracted from different seismic attributes are automatically selected and fused according to the correlation between the predicted anisotropy strengths and the measured fracture densities at well locations. Through seismic anisotropy extraction and automatic multisensitive attribute fusion, we can acquire a more comprehensive evaluation of different fracture types in a reservoir. The proposed fracture identification framework is successfully applied to a deep, tight sandstone reservoir in Southwest China. The predicted fracture distribution is closely related to the local structures in the target reservoir. The orientations of the most predicted fractures are consistent with the local maximum principal stress direction in this area, which is good for the opening and fluid filling of fractures. The fracture identification results will be used to guide hydrocarbon exploration activities in this region, such as exploration well deployment.