ZHANG Fei, LI Qiuzheng, JIANG Aming, et al. Application of shale oil 2D NMR logging evaluation in Huazhuang area of Gaoyou Sag[J]. Petroleum Reservoir Evaluation and Development, 2024, 14(5): 707-713.
ZHANG Fei, LI Qiuzheng, JIANG Aming, et al. Application of shale oil 2D NMR logging evaluation in Huazhuang area of Gaoyou Sag[J]. Petroleum Reservoir Evaluation and Development, 2024, 14(5): 707-713. DOI: 10.13809/j.cnki.cn32-1825/te.2024.05.005.
The mud shales of the second member of the Paleogene Funing Formation in the Gaoyou Sag of Subei Basin are distinguished by their complex pore structures
well-developed bedding
and pronounced heterogeneity. These characteristics complicate the accurate evaluation of pore and fluid types using conventional well logging techniques. However
two-dimensional nuclear magnetic resonance(2D NMR) logging presents distinct advantages in fluid identification. The study incorporates the “Blind Source Separation” signal processing technique to perform data clustering analysis. Subsequently
a fluid zone model is applied to determine the fluid content with different movable properties in the reservoir space of the Huazhuang area. The findings demonstrate that 2D NMR logging significantly improves the accuracy of reservoir fluid evaluation
yielding reliable estimates of total porosity
effective porosity
oil saturation
and movable oil volume content. When compared to core analysis
the average absolute errors for effective porosity and oil saturation are minimal
at 0.4% and 7.3%
respectively. Further analysis of fluid properties within the primary lithologies of the second member of the Funing Formation reveals that the felsic-calcitic-dolomitic-mixed shales possess superior physical properties
oil volume content
and mobility. These characteristics render them as prime targets for shale oil exploration in the region. The successful application of 2D NMR logging in the Huazhuang area not only addresses the challenges associated with evaluating shale oil porosity and saturation but also provides essential insights for selecting “sweet spot” districts and forecasting reservoir production.