Abstract
Seismic reflection coefficient equation for the fluid-saturated porous reservoirs under the effect of in situ stress is of great importance to broad fields such as geofluid discrimination, in situ stress prediction, and safe production. However, the stress effect on seismic reflection coefficient in porous reservoirs is poorly understood. To fill this knowledge gap, an approximate seismic reflection coefficient equation for fluid-saturated porous reservoirs under horizontal stress was proposed to model the natural effect of horizontal stress on seismic reflection response. We first revisited the acoustoelasticity (AE) theory and used it to characterize the impact of horizontal stress on skeleton anisotropy. Then, the effective elastic stiffness tensor and the corresponding spatial perturbation in the stressed fluid-saturated porous reservoirs were established under the assumption of fluid incompressibility, which were further employed to derive the approximate seismic reflection coefficient equation based on the elastic inverse scattering theory. By comparing our equation to the exact one, we confirmed its validity within the moderate incidence angles and stresses. The effects of horizontal stress on the P-wave amplitude variation with angle and azimuth (AVAZ) characteristics and seismic response were thoroughly investigated. It was shown that the horizontal stress significantly influenced the amplitude magnitude and seismic phases. Furthermore, the derived reflection coefficient equation was inserted into the Bayesian inversion scheme to estimate the geofluid indicator and other elastic parameters. Synthetic test and filed application showed a reasonable agreement between the inverted result and drilling data, which illustrated the feasibility and stability of our inversion method.
Paper Information：
Chen Fubin, Zong Zhaoyun*, Lang Kun,Li Jiayun, Yin Xingyao,Miao Zhiwei, Xiao Wei, 2024. Geofluid discrimination in stress-induced anisotropic porous reservoirs using seismic AVAZ inversion. IEEE Transactions on Geoscience and Remote Sensing, 5931714. DOI: 10.1109/TGRS.2024.3477943
