Density stability estimation method from pre-stack seismic data

Abstract

As one of the most important elastic parameters in seismic exploration, density plays an important role in lithology identification and hydrocarbon indicator. Based on the approximate Zoeppritz equation, the density term is usually estimated using AVO/AVA (Amplitude variation with offset/incident angle) inversion. However, the small contribution of density to the reflection coefficient or the strong correlation with other parameters in the approximate equation renders the density estimation tough. By analyzing the contribution of density to reflection coefficient in different approximate equations, Aki-Richards is finally selected as the inversion equation, in which the density term has a higher contribution to the reflectivity. The conventional inversion approach is to invert several parameters simultaneously on the basis of approximate equation. The same convergence criterion is used for the estimation of the three parameters, but the density estimation error fails to converge to the minimum due to its small sensitivity, which makes density inversion unstable. Furthermore, the conventional inversion methods involve the inverse of large matrix, which also reduces the stability of density estimation. Therefore, a new form of Aki-Richards approximate reflection equation is derived on the assumption that the inverse of coefficient matrix exists. The reflectivity of elastic parameter is independently expressed as the weighted superposition of seismic reflection coefficients at different incident angles. Then utilizing well logging data and seismic reflection coefficient estimated by elastic impedance inversion as constraints, the inverse of coefficient matrix in inversion equation is estimated directly, which avoids inverse of large matrix and improves the precision of density estimation. In physical, the weighted coefficients directly reflect the contribution of elastic parameters reflectivity to seismic data. Finally, different experimental examples show that proposed method can stably estimate density term.