Abstract：The existing linearized rock-physics inversion estimates porosity, shale content and saturation by using the P-wave velocity, S-wave velocity and density obtained by pre-stack elastic inversion. Compared with the P- and S-wave velocities, density contributes less to the reflection coefficient. Thus, the estimation of density requires a larger range of angles of seismic gathers. For deeply buried reservoirs, the estimation of density is unreliable due to the small reflection angles of pre-stack seismic gathers. In this case, the three-parameter inversion equation of rock-physics is underdetermined, and one cannot obtain a unique solution. This restricts the prediction of deeply buried reservoirs by existing linearized rock-physics inversion. Therefore, this paper proposes a method of porosity inversion for these reservoirs based on an iterative algorithm. First, the linear relationships of P- and S-wave velocities with porosity and shale content are deduced utilizing a linearized rock-physics model. Then, the objective function of iterative inversion is constructed in light of the Bayesian theory and solved by dichotomy. At last, the method is tested on synthetic seismic data and real data. Results show that it is independent of the density term and can well predict porosity with the iterative algorithm for deeply buried reservoirs with two phases of oil and water.
田军, 刘永雷, 徐博, 白建朴, 李青霖. 深埋储层孔隙度迭代反演方法[J]. 石油地球物理勘探, 2022, 57(3): 666-675.
TIAN Jun, LIU Yonglei, XU Bo, BAI Jianpu, LI Qinglin. A method for porosity prediction of deeply buried reservoirs based on iterative inversion. Oil Geophysical Prospecting, 2022, 57(3): 666-675.
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