Abstract：The stratigraphic distribution of Permian volcanic rocks in the Jingyan area of southwest Sichuan is stable,and the seismic reflection is characterized by "two peaks and one trough" between them overall.There are two sets of reservoirs developing in the volcanic rocks, and the upper reservoir is composed of multiple layers with thicknesses less than 7 m.Limited by conventional seismic resolution,seismic attribute and wave impedance inversion cannot effectively identify the distribution of the two sets of reservoirs.However,porosity can represent reservoir characteristics and has a good fitting relationship with wave impedance.Therefore,seismic data with extended frequency and the nonlinear inversion of porosity with a neural network are used to predict the upper and lower volcanic reservoirs.With the spectral decomposition technology, the seismic data is transformed into low-,medium-,and high-frequency data volumes,and the corresponding frequency-divided seismic attributes are obtained.On this basis,neural network inversion is performed to establish the nonlinear relationship between porosity and frequency-divided seismic attributes.Finally,high-resolution porosity inversion results are obtained.The prediction results show obvious improvement in both vertical and horizontal resolution. The results of neural network inversion are consistent with the actual situation of drilled wells:Two sets of volcanic reservoirs mainly develop in the middle and lower part vertically and are distributed in the west of the study area horizontally.The research results can guide later exploration evaluation or development well deployment.
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