Seismic facies analysis is an important tool for quantitative interpretation. It is usually based on machine learning technique to integrate well logs and seismic attributes. For unconventional reservoirs, the target layer is usually quite thin, which increase the difficulty of facies recognition. In order to improve the accuracy and robustness of unconventional facies analysis, we purpose to utilize a new category of seismic attributes, sedimentary cycle components, as the constraint of the optimization function. In the traditional seismic facies analysis, it is of great importance to use well logs as the supervised information, but the well logs are only at a few limited locations. Seismic data has the best coverage, so we adopt the sedimentary information from seismic decomposition as a supportive attribute in the machine learning process. As a time-frequency analysis technique, Hilbert-Huang transformation (HHT) is introduced into seismic facies analysis as the sequence stratigraphic constraints. But the data-driven HHT method has some drawbacks. Recently, varitional mode decomposition (VMD) method is proposed to separate more robust and reasonable intrinsic modes from the data. We use VMD to decompose seismic data into Intrinsic Mode Functions (IMF), and different IMFs have different characteristics and indicate different sedimentary information at different geological time scales, which can be used as the supervised information. We analyze different kinds of typical models of sedimentary cycle and their IMFs to verify the reliability and precision of the proposed method. Then in the field applications, we apply the sedimentary components from VMD as a geological time constraint in the self-organizing map (SOM) based seismic facies analysis. The field applications on Barnett shale/ Marble falls limestone show good correspondence between facies classification results and the logging data, with more reasonable unconventional layering features.
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