Seismic Sedimentology and Thin-Bedded Sandbody Characterization in the First Member of the Shawan Formation, Chepaizi Uplift, Junggar Basin, NW China: Implications for Sustainable Subsurface Investigation

The first member of the Shawan Formation (N1s1) in the Chepaizi Uplift, Junggar Basin, is characterized by thin-bedded sandstone–mudstone successions and rapid lateral facies changes, making sandbody characterization challenging. This study integrated well logs, mud-logging data, and 3D seismic data from S1W to establish a sequence-stratigraphic framework and interpret depositional architecture using 90°-phase seismic data, stratal slices, and seismic attributes. The interval was subdivided into three fourth-order sequences (SQN1s11, SQN1s12, and SQN1s13), showing an upward transition from sandstone-rich to mud-dominated deposits. Four depositional systems were identified: shallow braided river delta, meandering river, terminal fan, and shore–shallow lacustrine systems. Their distribution and migration were associated with sediment supply, changes in accommodation, and inherited paleogeomorphology. Well-log analysis showed that a channel-dominated interval in SQN1s11 had a sand ratio exceeding 40%, whereas the overlying sequences at the same location were dominated by finer-grained floodplain deposits. Through comparative analysis of seismic attributes and well–seismic calibration, the minimum-amplitude attribute was selected for sandbody characterization. Stratal slices of this attribute were used to trace composite seismic responses associated with sand-rich intervals and characterize their lateral distribution. The resulting maps delineated variations in channel-belt morphology and delta-front distribution among the sequences. These findings improve our understanding of depositional-system evolution and demonstrate the potential of integrating existing well and seismic data for thin-bedded sandbody characterization in shallow lacustrine settings. By improving the geological screening of sand-prone intervals using existing subsurface datasets, this approach may support more targeted exploration planning and resource-efficient subsurface investigation.