Prediction of thin sand body reservoirs using facies-model-constrained stochastic optimization inversion

Globally, subtle hydrocarbon reservoirs in petroliferous basins have always been challenging targets for exploration research, with thin sand body reservoir prediction being a key focus in this field. Thin sand body reservoirs typically manifest as thin interbeds of sandstone and mudstone. Current seismic inversion techniques struggle to accurately characterize the distribution patterns of such thin sand body reservoirs in the subsurface, necessitating novel inversion methods. This study proposes a stochastic optimization inversion method for thin sand bodies based on facies-model constraints. Dynamic forward modeling is conducted using typical thin sand body patterns to establish reasonable identification templates for superimposed configurations. The seismic data are subjected to steerable pyramid processing to achieve a multi-scale representation. By incorporating sedimentary facies analysis data, a facies-constrained volume is created. The acoustic parameter differences between the thin sand body reservoirs and the mudstone are analyzed, followed by stochastic optimization inversion of the sensitive parameters and interpretation of the inversion results. The inversion results for beach-bar thin sand body reservoirs in the Yingmaili area of the Tarim Basin show strong consistency with well-based sand body correlations and planar sedimentary facies distributions. Blind well validation demonstrated prediction accuracies of 63% for sand bodies thicker than 4 m and 84% for those exceeding 10 m. In comparison, conventional inversion methods achieve a prediction accuracy of only approximately 50% for thin sand bodies ranging from 4 m to 10 m in thickness and approximately 77% for those exceeding 10 m. The new methodology demonstrates improved prediction accuracy for thin sand bodies, thereby providing more reliable support for interpreting and evaluating the hydrocarbon potential of thin sand body reservoirs. This study achieves high-accuracy prediction of thin sand bodies and provides a novel methodology for the detailed characterization of thin sand bodies in beach-bar sedimentary basins worldwide.