CNN-Based Seismic Multi-Attribute Fusion for Quantitative Cumulative Sandstone Thickness Prediction in Ultra-Deep Reservoirs Under Sparse Well Control

Ultra-deep hydrocarbon formations in northwestern China are critical exploration targets, but their reported measured depths (MD) exceeding 6700 m, low exploration degree, and extremely sparse well control restrict efficient hydrocarbon exploration. Member-level cumulative sandstone thickness provides a measure of sandstone abundance within a stratigraphic member. Conventional methods face difficulties in representing nonlinear relationships between seismic data and cumulative sandstone thickness under sparse well control. To address these problems, this study proposes a CNN-based seismic multi-attribute fusion method for cumulative sandstone thickness prediction in ultra-deep, sparse well areas. Six model inputs were selected empirically with reference to attribute–thickness and inter-attribute Pearson correlations. A geological continuity-constrained augmentation strategy constructs 15 × 15 × 6 multi-channel input patches. The additional patches inherit existing well–member labels and do not add independently measured thickness values. For six well–member units from ss1/wt1, averaging the nine patch predictions for each unit gives MAE = 3.51 m, RMSE = 4.42 m, MSE = 19.51 m2, and R2 = 0.7653. These are descriptive results from only two wells whose labels also informed model selection; they do not establish independent predictive accuracy or spatial generalization. A preliminary interpretation of the predicted thickness patterns, based on the regional geological setting, suggests westward progradation and eastward retrogradation associated with fan delta and braided river delta systems. Areas with predicted cumulative sandstone thicknesses of 30–60 m in interpreted delta front zones are considered potentially favorable exploration targets requiring further petrophysical evaluation. This case study presents a workflow for cumulative sandstone thickness prediction in the studied ultra-deep, well-sparse area; its spatial generalization and applicability beyond the evaluated dataset remain unestablished.