Seismic Fluid Identification Method Research Of Tight Sandstone Reservoir

Tight sandstone reservoirs have low porosity,low permeability and complex pore structure,which makes the fluid factor based on fluid elastic parameter can not meet the accuracy requirement of reservoir prediction well.Therefore,finding more accurate fluid factor is the core of fluid identification research in tight sandstone reservoir.The pore fluid flow can cause dispersion and absorption of seismic wave.Therefore,the dispersion property can be used to identify fluid.The traditional AVO technology is based on the Gassmann theory.The Gassmann theory assumes that the rock has high porosity and high permeability,and the viscosity of fluid is zero,which without considering the dispersion and absorption of seismic waves.Therefore,the traditional technology has certain limitations for fluid identification.The frequency-dependent AVO inversion based on mesoscopic-scale rock physics theory introduces the frequency information to traditional inversion which can obtain the dispersion property to identify fluid.In this paper,we study on the seismic fluid identification of tight sandstone reservoir based on frequency-dependent AVO inversion.In this paper,we first analysis the relationship between dispersion characteristic and physical parameters through the mesoscopic-scale Johnson model,and discusses the main factors of dispersion,which provide the theoretical support for inversion.Then,we study on the inversion spectrum decomposition which has high precision based on traditional time-frequency analysis,and obtain frequency-dependent reflection coefficients as the input data of inversion.Finally,we dervie the frequency-dependent AVO inversion equation based on Russell approximation,and established the target function under Cauchy constraint,the IRLS optimization algorithm is used to solve the inversion problem to obtain the frequency-dependent fluid term which combined with the traditional inversion to construct a fluid factor with good stability and highresolution.Model tests and actual seismic data applications show that the new fluid factor has better characterization ability for fluid-contain reservoirs and higher accuracy of fluid identification.