Lithofacies Classification and Sweet Spot Development Model for Marine Shale in the Lower Cambrian Qiongzhusi Formation, Sichuan Basin

By integrating core samples, experimental data, and logging information, we adopted a comprehensive lithofacies classification method that incorporates total organic carbon (TOC), sedimentary structures, lithology, and brittleness. This classification scheme is both detailed and operationally practical. Based on analyses of Wells Z201 within the Deyang–Anyue aulacogen basin of the Sichuan Basin and WY1H at the aulacogen margin, seven lithofacies types were identified, with key favorable lithofacies highlighted. The brittle siliceous shale lithofacies (RLI) in the central aulacogen stands out as the most favorable reservoir in the region. It is characterized by high porosity, significant gas content, high brittleness, high lamination density, and thick depositional layers. In contrast, the organic-rich, laminated, moderate-brittleness silicic/argillaceous mixed shale (RLVI) lithofacies at the aulacogen margin, while exhibiting moderate porosity, is notable for its high gas content and high lamination density, making it the preferred lithofacies on the margin. Additionally, thinly interbedded lithofacies such as organic-poor, strongly brittle, silty shale (PI) and organic-poor, laminated, moderately brittle silicic/argillaceous mixed shale (PLVI) lithofacies with RLVI and organic-medium, bedded, strongly brittle, siliceous shale (MBI) lithofacies represent a new type of sweet spot lithofacies. Factors such as sea-level fluctuations, terrigenous debris influx, upwelling currents, paleoclimate, and hydrothermal activity played crucial roles in controlling the enrichment and preservation of organic matter, leading to a developed model for the sweet spot lithofacies in the Qiongzhusi Formation. This research establishes a scientific foundation for improving the exploration and production efficiency of the Qiongzhusi Formation shale gas play while contributing to the advancement of lithofacies classification systems for marine shale reservoirs and the refinement of “sweet spot” prediction methodologies.