Research On Multi-parameter Inversion Algorithm For Rock Physics Properties Based On Porelastic Wave Model

In reservoir description/characterization,quantitative evaluation of rock physics properties is of great significance.In this paper,the sensitivity of such poroelastic rock physics properties to various seismic viscoelastic attributes is studied,including the velocities,quality factors,and density.Because we design a generalized dynamic poroelastic model,our analysis results are applicable to most kinds of rocks over a wide range of frequencies.For the inversion process,we use an oriented Monte Carlo method called the neighborhood algorithm(NA).This method needs only two control parameters: the number of models generated for each iteration and the resampling size of Voronoi cells.The viscoelastic attributes computed by poroelastic forward modeling are used as input to a semiglobal optimization inversion code to estimate poroelastic properties,such as porosity,solid frame moduli,fluid phase properties,and saturation.Our sensitivity studies show that it is best to build an inversion system with enough input data to obtain accurate estimation.However,simultaneous inversion for the whole set of poroelastic parameters is problematic due to the large number of parameters.Therefore,we restrict the sensitivity tests to the estimation of specific poroelastic parameters by making appropriate assumptions on the fluid content and/or solid phases.After making use of well data or regional geology knowledge to realize a priori assumption,the studies find that: As long as there are enough input data,the estimation of frame properties parameters is accurate;the accuracy of permeability or pore fluid saturation prediction value largely depends on the use of attenuation data;the bulk modulus of fluid can be accurately inverted,while the sensitivity of other fluid characteristics is low;if there is introducing error in rock physical characteristics in prior conditions,the estimations of parameter will change linearly Finally,the uncertainty analysis of seismic input data shows that even if the inversion system is stable,if the input data is wrong,more input data need to be added to obtain accurate estimates.