The lithofacies of shale oil within the Dongying Sag of the Jiyang Depression are distinguished by their complex lithology

strong heterogeneity

and marked regional distribution variances. Current logging methods inadequately identify and evaluate the lithofacial characteristics of shale oil in this region. This study delves into the lithofacies characteristics of the upper submember of Chunhuazhen Formation of the fourth member of the Shahejie Formation in Dongying Sag

employing core calibration logging integrated with core

thin section

experimental analysis

and testing data. Utilizing the “three terminal elements and four elements” shale lithofacies partitioning scheme as a guiding principle

this research selects responsive characteristics of different lithofacies logging and sensitive parameter logging curves to develop an appropriate shale oil logging lithofacies partitioning method. The approach combines stratification

clustering through Agglomerative Hierarchical Clustering（AHC）

Fisher discriminant analysis

wavelet frequency extraction

and genetic optimization neural networks to discern the rock composition

sedimentary structure

rock texture

and organic matter content of different lithofacies. This methodology addresses the challenges posed by complex lithology

limited logging resolution

incomplete special logging data

and inadequate Total Organic Carbon（TOC）model accuracy. By identifying the “four characteristics” of lithofacies

the study establishes a quantitative logging identification method and technology for shale oil lithofacies in Dongying Sag

pinpointing concentrated lithofacies segments. The findings provide a critical geological basis for the large-scale exploration and development of shale oil in the region.