Characterization of complex fuvio–deltaic deposits in Northeast China using multi‑modal machine learning fusion

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Scientific Reports
Due to the lack of petroleum resources, stratigraphic reservoirs have become an important source of future discoveries. We describe a methodology for predicting reservoir sands from complex reservoir seismic data. Data analysis involves a bio-integrated framework called multi-modal machine learning fusion (MMMLF) based on neural networks. First, acoustic-related seismic attributes from post stack seismic data were used to characterize the reservoirs. They enhanced the understanding of the structure and spatial distribution of petrophysical properties of lithostratigraphic reservoirs. The attributes were then classifed as varied modal inputs into a central fusion engine for prediction. We applied the method to a dataset from Northeast China. Using seismic attributes and rock physics relationships as input data, MMMLF was performed to predict the spatial distribution of lithology in the Upper Guantao substrata. Despite the large scattering in the acoustic-related data properties, the proposed MMMLF methodology predicted the distribution of lithological properties through the gamma ray logs. Moreover, complex stratigraphic traps such as braided fuvial sandstones in the fuvio–deltaic deposits were delineated. These fndings can have signifcant implications for future exploration and production in Northeast China and similar petroleum provinces around the world.
This article is published in Scientific Reports, (2020) 10:13357 ;
Scientific Reports, (2020) 10:13357 ;