Abstract: The main gas-bearing layer of Zhongmu sag has a relatively well-developed medium-small scale fractures. The complex geological structures in this area, coupled with the shielding effect of the coal seams, have led to both the resolution and the signal-to-noise ratio of the seismic exploration results being lower than the ideal values. The fracture parameters predicted by conventional methods are insufficient to characterize the development of local medium-small scale fractures. This has led to the need for a more suitable method. To address this issue, this paper refined and enhanced the 3D seismic data using maximum likelihood techniques to obtain the initial fracture orientation and fracture density. Based on the initial fracture orientation and density, the orientation guidance field was established, and a random discrete fracture network model was calculated through the orientation guidance field and fracture density. The random discrete fracture networks were connected to establish continuous random discrete fracture network models tailored to Zhongmu area, thereby reflecting the development patterns and distribution characteristics of medium-small scale fractures. And a comparative analysis was made between random discrete fracture network models and continuous random discrete fracture network models. It indicates that the threshold of the connected fracture distance has a significant impact on the modeling of random discrete fracture networks. The network of medium-small scale fractures in Zhongmu area is predominantly orientated in the direction of NEE, i.e., the azimuth of 60°-80°. The random discrete fracture network is coincided with the fractures in the layer. The orientation of the rose diagrams of the fractures located in the surrounding wells is well consistent with that of the fracture network model. The model yielded positive results in fracture prediction within the Lower Paleozoic fracture-developed zone of a pilot area in the south North China basin.

Key words: Zhongmu sag, medium-small scale fractures, orientation guidance field, random discrete slit network model, continuous random discrete seam network characterization technology