Deep carbonate gas reservoir sweet spot identification with seismic data based on dual-factor control of sedimentary facies and fault system

Abstract

Deep carbonate reservoirs are attractive targets for gas development. These reservoirs are deeply buried, and commonly possess strong heterogeneity and poor seismic data quality, making the identification of favorable production areas (“sweet spots”) challenging. Furthermore, sedimentary facies and fault systems markedly impact reservoir quality, and identifying these features in seismic data is also crucial for sweet spot identification. To solve these problems, we propose a dual-factor-controlled sweet spot identification method with two steps. First, sedimentary facies and faults are identified separately at different seismic scales using different attributes by the steerable pyramid (SP) method. The SP method decomposes the original seismic data into high-frequency and low-frequency data. The amplitude attributes from high-frequency data are used to identify sedimentary facies, and coherence attributes based on low-frequency data are used to characterize the fault systems. Second, after separately identifying the sedimentary facies and faults, the two attribute volumes are merged together to identify reservoir sweet spots. The results are verified by using well production data. The results of a field study in the Dengying Formation deep carbonate reservoir in the central Sichuan Basin, China, indicate that reservoir sweet spots are primarily developed in ideal sedimentary facies along strike-slip fault systems. Sedimentary facies generally control the type and distribution of reservoirs, whereas strike-slip fault systems control the migration and accumulation of gas. In addition, the fault systems serve as karst channels that further improve the reservoir properties. The proposed dual-factor method might help to maximize exploration potential in deep carbonate reservoirs with similar settings.