Abstract
The delineation of shale oil sweet spots is a crucial step in the exploration of shale oil reservoirs. A single attribute such as total organic carbon (TOC) is conventionally used to evaluate the sweet spots of shale oil. This study proposes a probabilistic Fisher discriminant approach for estimating shale oil sweet spots, in which the probabilistic method and Gaussian mixture model are incorporated. Statistical features of shale oil facies are obtained based on the well log interpretation of the samples. Several key parameters of shale oil are projected to data sets with low dimensions in each shale oil facies. Furthermore, the posterior distribution of different shale oil facies is built based on the classification of each shale oil facies. Various key physical parameters of shale oil facies are inversed by the Bayesian method, and important elastic properties are extracted from the elastic impedance inversion (EVA-DSVD method). The method proposed in this paper has been successfully used to delineate the sweet spots of shale oil reservoirs with multiple attributes from the real pre-stack seismic data sets and is validated by the well log data.
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Acknowledgements
We would like to acknowledge the sponsorship of the National Natural Science Foundation of China (Grant Nos. 41974119 and 42030103) and Science Foundation from Innovation and Technology Support Program for Young Scientists in Colleges of Shandong Province and Ministry of Science and Technology of China.
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Luo, K., Zong, Z. Probabilistic Fisher discriminant analysis based on Gaussian mixture model for estimating shale oil sweet spots. Front. Earth Sci. 16, 557–567 (2022). https://doi.org/10.1007/s11707-021-0926-5
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DOI: https://doi.org/10.1007/s11707-021-0926-5