Abstract
Shale oil, as a crucial component of unconventional hydrocarbon resources, is distinguished by its broad distribution and significant potential, yet geological analysis remains constrained by ultra-low porosity and permeability, pronounced heterogeneity, and structural complexity. The emergence of big data technologies has introduced transformative perspectives and tools to address these challenges. This paper systematically reviews multi-source heterogeneous datasets in shale oil exploration and development—including seismic surveys, well logging, core experiments, and production monitoring—while examining their structural features and quality control requirements. On this basis, it highlights key big data-driven approaches such as data integration and governance, feature extraction and pattern recognition, machine learning-based predictive modeling, and real-time processing with dynamic model updating. Practical applications are summarized in sweet spot identification, quantitative reservoir heterogeneity characterization, intelligent fracture interpretation, and integrated geological–engineering analysis. The paper further discusses core challenges, notably multi-source data fusion and interoperability, model interpretability and physical consistency, the effective incorporation of geological prior knowledge, and interdisciplinary collaboration. Finally, it outlines future research directions and provides systematic theoretical and methodological guidance for the integration of big data with shale oil geological analysis. These efforts aim to foster more intelligent, efficient, and refined development of unconventional petroleum geology.
