关键词: 厚度梯度, 有效高频体, 薄层, 物性预测, 复杂重力流, 莺歌海盆地, 中新统, 黄流组

Abstract: The mid-to-deep reservoirs of the Miocene Huangliu Formation in the F gas field of Yinggehai Basin are dominated by complex gravity flow deposits. The primary producing interval, namely the Unit-a of submember Ⅰ of Member 1 of Huangliu Formation, features small thickness and pronounced heterogeneity in physical properties. To address the ambiguous geological implications of seismic amplitude attributes and challenges in sweet-spot reservoir prediction, combined spectral expansion was employed to enhance the resolution of original seismic data. This was integrated with fine-scale thickness characterization via broadband inversion, and three thickness gradients were partitioned using λ/8 and λ/4 as boundaries. Subsequently, by utilizing effective high-frequency seismic data volume derived from multi-wavelet decomposition and reconstruction, high-frequency amplitude thresholds for effective reservoirs were established. This methodology enabled the semi-quantitative prediction of physical properties for thin reservoirs with thicknesses ranging between λ/8 and λ/4. The results indicate as follows. (1) The optimized combined spectral expansion technology based on spectral shaping balances the enhancement of thin reservoir resolution with the preservation of original sand-body contact relationships, making it suitable for identifying thin reservoirs in complex gravity flows. (2) The amplitude of thin reservoirs is influenced by the coupling of thickness and physical properties, and effective high- frequency information within seismic signals serves as a significant indicator of thin reservoir physical properties. (3) Reservoir conditions vary significantly across different thickness gradients in gravity flow deposits. High-quality reservoirs can be effectively identified by optimizing frequency bands sensitive to physical properties based on the planar distribution of thin reservoirs. The study concludes that high-frequency-indicated prediction results for thin reservoir physical properties based on thickness gradient partitioning are reliable, having effectively guided the deployment of development wells and providing an important reference for the physical property prediction of thin reservoirs in analogous oil and gas fields.

Key words: thickness gradient, effective high-frequency seismic data volume, thin bed, physical property prediction, complex gravity flow, Yinggehai Basin, Miocene, Huangliu Formation