The global technically recoverable resources of shale oil are estimated at approximately 251.2 billion metric tons,derived from 157 organic-rich shale units across 116 basins. Internationally,shale oil exploration has largely focused on marine shale successions,whereas in China,lacustrine shales represent the primary target. The assessment of shale oil enrichment intervals is fundamentally governed by lithofacies palaeogeography. Lithofacies palaeogeographic reconstruction of lacustrine shale strata relies on quantitative datasets derived from outcrop,core,and geophysical analyses. Critical aspects of such reconstructions include considerations of temporal and spatial scales,single-factor analysis,and the application of conceptual depositional models. Recent progress in the study of shale laminae offers valuable single factors and diagnostic methods for interpreting depositional environments in palaeogeographic studies. Significant variations in reservoir properties and shale oil occurrence are observed among different lamina types. Consequently,palaeogeographic mapping based on lamina combinations and shale microfacies analysis represents an emerging research trend. Future research should aim to address the heterogeneous characteristics of lithofacies and organic matter distribution in lacustrine shale strata. Future studies should aim to resolve the heterogeneous distribution of lithofacies and organic matter in lacustrine shales,with priority given to: (1)delineating the spatial architecture of organic-rich shales within high-resolution chronostratigraphic frameworks,(2)characterizing mineral-organic composite structures,(3)establishing standardized microfacies classifications,and(4)developing depositional process simulations integrated with intelligent mapping techniques. An integrated approach combining modern sedimentological observations,sedimentary process simulations(both physical and numerical),organic petrology,and biomarker analysis is essential to reassess the sedimentary microfacies and organic enrichment mechanisms in lacustrine shale strata. This will facilitate the development of revised depositional models that accurately reflect the transport-deposition dynamics of fine-grained sediments,palaeotopographic constraints,and bioevolutionary context. By combining “inversion” and “forward modeling” methods and lever aging artificial intelligence technology,and incorporating key sweet-spot evaluation parameters,it is possible to quantitatively predict and intelligently map the spatiotemporal distribution of different lamina combinations. This methodology will allow for precise lithofacies palaeogeographic characterization of lacustrine shale strata and provide scientific guidance for the optimization of shale oil enrichment areas/layers.