The core of the exploration and development of unconventional oil and gas resources, such as shale oil, lies in the effective identification and scale utilization of the sweet spot. Oil saturation is an important parameter in evaluating the sweet spot. Aiming to solve the many current problems of oil saturation logging evaluations of shale oil reservoirs, this study outlines the research progress of oil saturation logging evaluations of shale oil from the three aspects, namely, the electrical method, non-electrical method, and machine learning, through researching the literature and practical applications. At the same time, several typical saturation models are applied to shale oil reservoirs in the Qingshankou Formation of the Gulong Depression, and applicability analyses are conducted. Lastly, the advantages and disadvantages of each oil saturation calculation model are summarized, and suggestions are given for conducting research using each type of method. This study has certain significance in the selection of oil saturation logging evaluation methods for shale oil reservoirs and provides directions for improvement.
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