Deeply buried fractured reservoirs have evolved into significant oil and gas potential in many basins of the world.
However, fracture prediction in deeply buried carbonate reservoirs has always been challenging. Fracture prediction
in the deep-buried carbonate structure of North China is problematic because of multiphase tectonic
movements, variable sediment lithology, and complex diagenesis. Because of deep burial depth and complex
heterogeneity, the resolution of seismic reflection data beneath the buried-structure is poor, making it challenging
to identify the fault reflection characteristics. This paper proposes a novel idea to identify natural
fractures in carbonate reservoirs using conventional logs with seismic reflection data. The proposed model can
also predict the fracture aperture and fracture density, a distinctive feature. Another novel hybrid model based
on deep-learning neural network (DNN) and cluster analysis is proposed to predict further the spatial variations
of lithology, porosity, and fracture parameters from seismic inversion. The proposed models provide valuable
insights that help determine fracture parameters in the Paleozoic strata and associated reservoirs through
quantitative analysis using petrophysics, rock physics, seismic inversion, and seismic attributes. The overlapping
of seismic interpreted fault networks and spatial variations of the inverted fracture parameters indicate a high
correlation of fracture development zones. The methodology proposed in this study presents a valuable template
valid for the characterization of fractured reservoirs in deeply-buried carbonate reservoirs throughout the world.