Sedimentological and diagenetic facies of tight sandstones in lacustrine delta-front: A case study of the Jurassic Lianggaoshan Formation, eastern Sichuan Basin - Yuan, Chengfang; Guo, Weixue; Cai, Laixing; Zeng, Yangjing; Zhang, Zhenkai; Liu, Yinglin; Yang, Tian (Frontiers in Earth Science, 2024)
In this study, taking the Jurassic Lianggaoshan Formation (J1l) tight sandstones in the eastern Sichuan Basin as an example, the types and well-logging responses of main sedimentological and diagenetic facies in the lacustrine delta-front are investigated based on summarizing the sedimentary characteristics and reservoir properties. Subsequently, further validation and application are conducted in the study area through machine learning.Research results show that the J1l lacustrine delta-front in the eastern Sichuan Basin mainly develops subaqueous distributary channel and mouth bar sand bodies, exhibiting typical densification reservoirs with porosity and permeability distributed between 0.48%-11.24% (av. 3.87%) and 0.0003-0.653×10 -3 μm 2 (av. 0.026×10 -3 μm 2 ), respectively. Strong compaction and strong cementation are the primary factors leading to densification, while chlorite coatings and weak dissolution play constructive roles in preserving some primary pores, creating a small amount of dissolution pores, and enhancing permeability. In terms of the manifestation, the content of pore-throats with a radius greater than 0.006 μm governs the reservoir quality. Furthermore, five types of diagenetic facies are identified in the J1l subaqueous distributary channels and mouth bars, including strong compaction facies (Type I), strong cementation facies (Type II), chlorite coating and intergranular pore facies (Type III), weak dissolution and intragranular pore facies (Type IV), and medium compaction and cementation facies (Type V). Overall, the thick and coarse-grained subaqueous distributary channels can be considered as the preferred exploration targets for tight oil and gas, with Type III and IV diagenetic facies being the most favorable reservoirs, characterized by well-logging responses of high AC and low GR, DEN and RT. Based on the fine division of sedimentological and diagenetic facies, establishing well-logging interpretation models and then employing machine learning to achieve sweet spot reservoir prediction can provide valuable insights for tight oil and gas exploration in regions lacking core data.