Architecture characteristics and characterization methods of fault-controlled karst reservoirs: A case study of the Shunbei 5 fault zone in the Tarim Basin, China

Abstract

Carbonate fault-controlled karst reservoirs are unique because they typically are deeply buried and have substantial heterogeneity throughout compared with sandstone or other carbonate reservoirs. Creating a characterization method is critical for the efficient development of this kind of reservoir. The Shunbei 5 fault zone is an example of a carbonate fault-controlled karst reservoir located in the Tarim Basin. We summarized a 3D architectural model of fault-controlled karst reservoir based on the outcrop, drilling, logging, and seismic data. We dissected the architectural characteristics and established a comprehensive technology series to characterize fault-controlled karst reservoirs. The results indicate that the strike-slip fault and its broken strata are the geologic basis for karst development. We divided the architectural elements of the fault-controlled karst reservoirs into five categories: fault core damaged zone, fracture-damaged zone, dissolution pore zone, large cavern zone, and cavern filling zone. The fault core damaged zones form along the main slip surface of the strike-slip faults, and its interior is composed of a fault core and damaged zone. We proposed U-net deep machine learning network based on the seismic data to predict the 3D distribution of the fault core damaged zone. The fracture-damaged zone is the weakest part of karstification with poor reservoir quality. The fracture-damaged zone’s 3D distribution was effectively characterized using the improved seismic data texture attribute. The dissolution pore zone is mainly distributed outward the fracture-damaged zone, with a medium karstification degree. The optimized seismic data energy envelope attribute can effectively characterize the 3D aspect of this region, with the large cavern zone representing the position with the most substantial degree of karstification. The reservoir quality depends on the fillings inside the cavern, and we predicted the large cavern zones based on the energy enhanced residual impedance properties.