Characterization of tight sandstone architecture in arid shallow-water delta distributary channels

Shallow-water deltas represent crucial reservoirs for tight gas， playing a significant role in petroleum exploration and development. Prediction of tight sandstone thickness distributions and hierarchical characterization of sedimentary architectures are essential for efficient reservoir exploitation， optimal well placement， and horizontal well fracturing. Taking the Shaximiao Formation in the Jinqiu Gas Field， Sichuan Basin as a case study， this research integrates multiple seismic attribute fusion methods and seismic inversion techniques to delineate sand bodies distributions and sedimentary architectures within the context of an arid-climate shallow-water delta model. Three hierarchical architectural scales (channel complexes， channels， and point bars) are investigated. The results demonstrate that: i) the RGB blending of spectral-decomposed seismic attributes can be used to delineate sand body boundary， while machine learning algorithms can be used for thickness prediction. Furthermore， intelligent seismic inversion techniques accurately define the spatial distribution of sand bodies. ii) Sand bodies in study area exhibit interwoven and meandering geometries trending from NE to SW， characterized by multistage development and encapsulation within thick mudstone layers. Within Zone 6， 16 distinct channel fills were identified， predominantly containing point bars and abandoned channels deposits. iii) Shallow-water deltas influenced by arid climatic conditions display stable， wide， and shallow distributary channels dominated by lateral accretion， sharing sedimentary characteristics akin to low-sinuosity meandering rivers. These findings provide critical insights for future exploration and development of tight gas reservoirs， underscoring the significance of advanced seismic prediction techniques for better understanding sedimentary architectures in arid shallow-water deltas.