基于多矿物分析的砂岩储层岩石物理参数反演OA

The study of rock physics parameter inversion methods is of great significance in seismic reservoir prediction.Well logging data serves as the primary source of information for this process.Using well logging data to invert rock physical parameters requires two prerequisites:first,knowledge of the rock's mineral composition,and second,the use of appropriate rock physics models.In practical applications,obtaining accurate mineral composition information can be challenging,thereby reducing the precision of the inversion.To address this issue,a Bayesian theory-based multi-mineral inversion method is employed to obtain the endpoint values and volume fractions of the minerals in the rock.Subsequently,a least-squares algorithm constrained by the critical porosity model,the Voigt model,and the Reuss model is used to invert the elastic moduli of the minerals and the pore aspect ratio.Results from actual well logging data show that the Bayesian theory-based multi-mineral inversion method can yield reasonable estimates of mineral volume fractions.Building on these estimates,the least-squares algorithm can further refine the inversion to yield more accurate estimates of the mineral elastic moduli and pore aspect ratios.This approach yields reconstructed P-and S-wave velocities closer to the measured values,thereby reducing the error between the predicted and observed values.