Abstract

The development of Pre−stack depth migration makes the imaging of the subsurface structure in the depth possible, which set a foun− dation for the study of amplitude variation with incident angle (AVA) inversion. This leads to the increasing demanding of the seismic inversion methods in the depth domain for guiding reservoir characterization. However, the conventional seismic inversion methods in the time domain are not suitable in the depth domain due to the seismic wavelet in the depth domain is depth−variant and depending on medium velocity. To address this issue, we proposed a pragmatic seismic inversion method for fluid factor in the depth domain with amplitude variation with incident angle gathers by using a true−depth wavelet on the process of seismic inversion. This wavelet is es− timated by converting the time wavelet to the depth wavelet with seismic velocity. To guide the fluid discrimination, the proposed method directly estimates the fluid factor from the pre−stack seismic data and all the process of the method is implemented in the depth domain. To deal with the weak nonlinearity induced by the velocity, the Bayesian inference, the prior information and model constraint are in− troduced in this seismic inversion method. Tests on synthetic data show that the fluid factor can be well estimated reasonably even with moderate noise. The field data example illustrates the feasibility and efficiency of the proposed method in application and the estimated fluid factor and shear modulus are in good agreement with the drilling results.

Highlights

  • The development of pre-stack depth migration has propelled to the forefront in investigations of the imaging of subsurface structure in the depth domain

  • The development of reservoir models and subsequent interpretation requires the transfer of the reservoir properties from time domain to the depth domain

  • We test the proposed amplitude variation with incident angle (AVA) inversion method in the depth domain with the synthetic seismic traces from real well logs which shows in the Figure 2 and the sampling interval is 4 m

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Summary

INTRODUCTION

The development of pre-stack depth migration has propelled to the forefront in investigations of the imaging of subsurface structure in the depth domain. Singh [2012] developed a deterministic inversion of seismic data in the depth domain He utilized the pseudo-depth transformation with constant velocities to handle the wavelet estimation. Pseudo-depth model was resampled in a constant depth interval, which might omit some reflection coefficients To address this issue, Wei et al [2017] proposed an analytic seismic wavelet to implement a convolution algorithm for seismogram synthesis in depth domain in terms of weighted superposition principle. The prevailing methods for estimating the fluid factors in time domain from pre-stack seismic data renders the rapid development of technology for reservoir forecasting and fluid discrimination [Smith and Gidlow, 2000; Quakenbush et al, 2006]. A test on a real data set demonstrates that the estimated results are in good agreement with the results of drilling results

SEISMIC WAVELET ESTIMATION IN THE DEPTH DOMAIN
AVA INVERSION IN THE DEPTH DOMAIN FOR FLUID FACTOR
SYNTHETIC EXAMPLE
REAL DATA EXAMPLE
CONCLUSIONS
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