  It is an extremely challenging task to precisely identify the reservoir
characteristics directly from seismic data due to its inherit nature. Here, we
successfully design a deep-learning model integrated with synthetic wedge
models to overcome the geophysical limitation while performing the
interpretation of thickness of sand bodies from a low resolution of seismic
data. Through understanding and learning the geophysical relationship between
seismic responses and corresponding indicators for sand thickness, the
deep-learning model could automatically detect the locations of top and base of
sand bodies identified from seismic traces by precisely revealing the lithology
distribution. Quantitative analysis and extensive validations from wedge models
and field data prove the robustness of the proposed methodologies. The true
sand thickness, identified from the deep-learning model, provides an extremely
useful guidance in enhancing the interpretation of lithological and
stratigraphic information from seismic data. In addition, the proposed
deep-learning approach eliminates the risks of over- and under-estimation of
net-to-gross with a significant improvement with respect to the accuracy.
