Abstract: This paper presents two methods of seismic data inversion for study of volcanic reservoir of Block Zao-35 in Huanghua depression: the well log constrained wave impedance inversion and the neural network-based multi-attribute inversion. The former is one of model-based seismic inversion, helpful to recognize the structure of volcano, laterally predict the volcanic rock and classify the volcanic process stage; the latter provides the ranges of thickness and physical property of volcanic rocks by the inversion, combined with lithology, well logs and seismic parameters. The results of inversion could be as bases for geologic modeling and reserves calculation of volcanic reservoir.

Key words: volcanic rock, reservoir, wave impedance, neural network, inversion, petrophysical property, parameter